{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<center><h1> HDDM: Hierarchical Bayesian Estimation of the Drift Diffusion model</h1><p>\n",
    "<h3>Thomas Wiecki</h3><p>\n",
    "<h3>Former: PhD at Brown University on Computational Psychiatry</h3><p>\n",
    "<h3>Current: Lead Data Scientist at Quantopian Inc.</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Contents\n",
    "\n",
    "* Features\n",
    "* Installation\n",
    "* First model\n",
    "* Group-wise conditions\n",
    "* Trial-by-trial effects\n",
    "* Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Why should you use it?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Bayesian parameter estimation -> Posteriors over parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## It's hierarchical.\n",
    "\n",
    "<img src=\"graphical_hddm.svg\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Informative priors based on literature\n",
    "\n",
    "<img src=\"hddm_info_priors.svg\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## ... all lead to better parameter recovery\n",
    "\n",
    "<img src=\"http://www.frontiersin.org/files/Articles/55610/fninf-07-00014-HTML/image_m/fninf-07-00014-g006.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Other reasons\n",
    "* Heavily optimized likelihoods for speed (minutes to couple of hours for complex models).\n",
    "* Tuned samplers (slice sampling) for fast convergence.\n",
    "* Trial-by-trial regressions allow estimation of influence of brain measures onto parameters.\n",
    "* Free & Open-source (BSD license)\n",
    "* Python (not Matlab)\n",
    "* Good software engineering practices (unittests, continuous integration)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## It's Roger Ratcliff approved\n",
    "\n",
    "\n",
    "\"We found that the hierarchical diffusion method [as implemented by HDDM] performed very well, and is the method of choice when the number of observations is small.\"<br>\n",
    "\n",
    "Roger Ratcliff, grandfather of the DDM, in a paper comparing all available tools to do DDM analysis. </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Prof. James Rowe (Cambridge University)\n",
    "\n",
    "\"The HDDM modelling gave insights into the effects of disease that were simply not visible from a traditional analysis of RT/Accuracy. It provides a clue as to why many disorders including PD and PSP can give the paradoxical combination of akinesia and impulsivity. Perhaps of broader interest, the hierarchical drift diffusion model turned out to be very robust. In separate work, we have found that the HDDM gave accurate estimates of decision parameters with many fewer than 100 trials, in contrast to the hundreds or even thousands one might use for ‘traditional’ DDMs. This meant it was realistic to study patients who do not tolerate long testing sessions.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Installation\n",
    "\n",
    "* Install the Anaconda Python distribution from Continuum. Available for all platforms.\n",
    "* Type: `conda install -c pymc hddm`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## First steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Importing the modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd  # Input, output and process tabular data\n",
    "import matplotlib.pyplot as plt  # Plotting\n",
    "import hddm  # Our toolbox\n",
    "\n",
    "print(hddm.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Loading data from csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subj_idx,stim,rt,response,theta,dbs,conf\r\n",
      "0,LL,1.21,1.0,0.65627512226100004,1,HC\r\n",
      "0,WL,1.6299999999999999,1.0,-0.32788867166199998,1,LC\r\n",
      "0,WW,1.03,1.0,-0.480284512399,1,HC\r\n",
      "0,WL,2.77,1.0,1.9274273452399999,1,LC\r\n",
      "0,WW,1.1399999999999999,0.0,-0.21323572605999999,1,HC\r\n",
      "0,WL,1.1499999999999999,1.0,-0.43620365940099998,1,LC\r\n",
      "0,LL,2.0,1.0,-0.27447891439400002,1,HC\r\n",
      "0,WL,1.04,0.0,0.66695707371400004,1,LC\r\n",
      "0,WW,0.85699999999999998,1.0,0.11861689909799999,1,HC\r\n"
     ]
    }
   ],
   "source": [
    "!head cavanagh_theta_nn.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### We use the ``hddm.load_csv()`` function to load this file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "data = hddm.load_csv(\"./cavanagh_theta_nn.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### This is what it looks like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subj_idx</th>\n",
       "      <th>stim</th>\n",
       "      <th>rt</th>\n",
       "      <th>response</th>\n",
       "      <th>theta</th>\n",
       "      <th>dbs</th>\n",
       "      <th>conf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>LL</td>\n",
       "      <td>1.210</td>\n",
       "      <td>1</td>\n",
       "      <td>0.656275</td>\n",
       "      <td>1</td>\n",
       "      <td>HC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>WL</td>\n",
       "      <td>1.630</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.327889</td>\n",
       "      <td>1</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>WW</td>\n",
       "      <td>1.030</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.480285</td>\n",
       "      <td>1</td>\n",
       "      <td>HC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>WL</td>\n",
       "      <td>2.770</td>\n",
       "      <td>1</td>\n",
       "      <td>1.927427</td>\n",
       "      <td>1</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>WW</td>\n",
       "      <td>1.140</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.213236</td>\n",
       "      <td>1</td>\n",
       "      <td>HC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>WL</td>\n",
       "      <td>1.150</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.436204</td>\n",
       "      <td>1</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>LL</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.274479</td>\n",
       "      <td>1</td>\n",
       "      <td>HC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>WL</td>\n",
       "      <td>1.040</td>\n",
       "      <td>0</td>\n",
       "      <td>0.666957</td>\n",
       "      <td>1</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>WW</td>\n",
       "      <td>0.857</td>\n",
       "      <td>1</td>\n",
       "      <td>0.118617</td>\n",
       "      <td>1</td>\n",
       "      <td>HC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>WL</td>\n",
       "      <td>1.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0.823626</td>\n",
       "      <td>1</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>LL</td>\n",
       "      <td>1.720</td>\n",
       "      <td>1</td>\n",
       "      <td>0.649154</td>\n",
       "      <td>1</td>\n",
       "      <td>HC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>WL</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0</td>\n",
       "      <td>0.093692</td>\n",
       "      <td>1</td>\n",
       "      <td>LC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subj_idx stim     rt  response     theta  dbs conf\n",
       "0          0   LL  1.210         1  0.656275    1   HC\n",
       "1          0   WL  1.630         1 -0.327889    1   LC\n",
       "2          0   WW  1.030         1 -0.480285    1   HC\n",
       "3          0   WL  2.770         1  1.927427    1   LC\n",
       "4          0   WW  1.140         0 -0.213236    1   HC\n",
       "5          0   WL  1.150         1 -0.436204    1   LC\n",
       "6          0   LL  2.000         1 -0.274479    1   HC\n",
       "7          0   WL  1.040         0  0.666957    1   LC\n",
       "8          0   WW  0.857         1  0.118617    1   HC\n",
       "9          0   WL  1.500         0  0.823626    1   LC\n",
       "10         0   LL  1.720         1  0.649154    1   HC\n",
       "11         0   WL  0.656         0  0.093692    1   LC"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting RT distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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My38j87ZHWFpTQ7nLxYejR3e4vbtzc/Fozd2+ZNDd8fUmRo4NQCmF1lp1VCZQ9xn8BXhX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\nWU+hVOs/xUjnchzuKgDq6+s5+6qruHT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C+zQu+07eKaYOuISKjWVU7a46EwwaUmMGKhi0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\nG5kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb04cb7940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = hddm.utils.flip_errors(data)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, xlabel=\"RT\", ylabel=\"count\", title=\"RT distributions\")\n",
    "for i, subj_data in data.groupby(\"subj_idx\"):\n",
    "    subj_data.rt.hist(bins=20, histtype=\"step\", ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Fitting a hierarchical model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 2000 of 2000 complete in 474.7 sec"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pymc.MCMC.MCMC at 0x7fcb0066d470>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Instantiate model object passing it our data (no need to call flip_errors() before passing it).\n",
    "# This will tailor an individual hierarchical DDM around your dataset.\n",
    "m = hddm.HDDM(data)\n",
    "\n",
    "# find a good starting point which helps with the convergence.\n",
    "m.find_starting_values()\n",
    "\n",
    "# start drawing 2000 samples and discarding 20 as burn-in\n",
    "m.sample(2000, burn=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Generating summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2.0575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_std</th>\n",
       "      <td>0.376146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.0</th>\n",
       "      <td>2.38334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.1</th>\n",
       "      <td>2.13225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.2</th>\n",
       "      <td>1.74836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.3</th>\n",
       "      <td>2.25216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.4</th>\n",
       "      <td>1.49291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.5</th>\n",
       "      <td>1.76831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.6</th>\n",
       "      <td>1.61111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.7</th>\n",
       "      <td>1.87703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.8</th>\n",
       "      <td>2.22648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.9</th>\n",
       "      <td>2.15667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.10</th>\n",
       "      <td>2.19983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.11</th>\n",
       "      <td>2.69749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.12</th>\n",
       "      <td>1.89781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a_subj.13</th>\n",
       "      <td>2.34687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v</th>\n",
       "      <td>0.412732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_std</th>\n",
       "      <td>0.274552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.0</th>\n",
       "      <td>0.115645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.1</th>\n",
       "      <td>0.618922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.2</th>\n",
       "      <td>0.51327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.3</th>\n",
       "      <td>0.140503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.4</th>\n",
       "      <td>0.90812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.5</th>\n",
       "      <td>0.476372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.6</th>\n",
       "      <td>0.433082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.7</th>\n",
       "      <td>0.0290477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.8</th>\n",
       "      <td>0.466359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.9</th>\n",
       "      <td>0.214938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.10</th>\n",
       "      <td>0.531898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.11</th>\n",
       "      <td>0.531417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.12</th>\n",
       "      <td>0.589736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>v_subj.13</th>\n",
       "      <td>0.199989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t</th>\n",
       "      <td>0.433964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_std</th>\n",
       "      <td>0.084089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.0</th>\n",
       "      <td>0.405995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.1</th>\n",
       "      <td>0.393955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.2</th>\n",
       "      <td>0.384193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.3</th>\n",
       "      <td>0.480034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.4</th>\n",
       "      <td>0.345045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.5</th>\n",
       "      <td>0.352416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.6</th>\n",
       "      <td>0.420171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.7</th>\n",
       "      <td>0.554495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.8</th>\n",
       "      <td>0.485506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.9</th>\n",
       "      <td>0.361947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.10</th>\n",
       "      <td>0.589183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.11</th>\n",
       "      <td>0.384821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.12</th>\n",
       "      <td>0.398009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>t_subj.13</th>\n",
       "      <td>0.49783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean\n",
       "a             2.0575\n",
       "a_std       0.376146\n",
       "a_subj.0     2.38334\n",
       "a_subj.1     2.13225\n",
       "a_subj.2     1.74836\n",
       "a_subj.3     2.25216\n",
       "a_subj.4     1.49291\n",
       "a_subj.5     1.76831\n",
       "a_subj.6     1.61111\n",
       "a_subj.7     1.87703\n",
       "a_subj.8     2.22648\n",
       "a_subj.9     2.15667\n",
       "a_subj.10    2.19983\n",
       "a_subj.11    2.69749\n",
       "a_subj.12    1.89781\n",
       "a_subj.13    2.34687\n",
       "v           0.412732\n",
       "v_std       0.274552\n",
       "v_subj.0    0.115645\n",
       "v_subj.1    0.618922\n",
       "v_subj.2     0.51327\n",
       "v_subj.3    0.140503\n",
       "v_subj.4     0.90812\n",
       "v_subj.5    0.476372\n",
       "v_subj.6    0.433082\n",
       "v_subj.7   0.0290477\n",
       "v_subj.8    0.466359\n",
       "v_subj.9    0.214938\n",
       "v_subj.10   0.531898\n",
       "v_subj.11   0.531417\n",
       "v_subj.12   0.589736\n",
       "v_subj.13   0.199989\n",
       "t           0.433964\n",
       "t_std       0.084089\n",
       "t_subj.0    0.405995\n",
       "t_subj.1    0.393955\n",
       "t_subj.2    0.384193\n",
       "t_subj.3    0.480034\n",
       "t_subj.4    0.345045\n",
       "t_subj.5    0.352416\n",
       "t_subj.6    0.420171\n",
       "t_subj.7    0.554495\n",
       "t_subj.8    0.485506\n",
       "t_subj.9    0.361947\n",
       "t_subj.10   0.589183\n",
       "t_subj.11   0.384821\n",
       "t_subj.12   0.398009\n",
       "t_subj.13    0.49783"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.gen_stats()[[\"mean\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Plotting the posterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting a\n",
      "Plotting a_std\n",
      "Plotting v\n",
      "Plotting t\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wiecki/miniconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
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z0Jc+ERGRQNnAyt3nu/vTheeLgBeA9RNWPQm4A3gz8xJKw3vllXqXQERCyhtKT3UleUid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\nbk7HgcXpuDCr7jr0q0/b9kOBR4Fj0ueH1xzP54C/n4oFeB4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb00451f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AKnkMv/CFbM9MvL3f/a5l2W7dopP5oYdm13/+81GydHzcZs6EAw7Ivl5OYOUe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\nOmv9yPLzEDGNLE/niafrvObO01W3WJ0eeC8iTgKdgfdaISJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb004e42b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BqQXLJkYe/xL4ZdIDxg3SeMstQbdTNapJTC81/16pfYc5JbW2WCVV7BhjxgR/\nz+w2qlh13IuP0wS1jWNVStIRzovVcVw91npRd68sqT1OYbnq9fmF3784hccIp7rZdNPS21fyvhRY\niYiU17RTYPSEnnQKlKhw1PR6BVZxE8cW23etgdW77+Y/TzKOUFR00MZ6+P73i98QUGsAGZfjtWxZ\n0N1Y+DmEigVWf/5z/tx4cZ9bPS7qtb7nwq7AYgHJllvWrzzFuq37F97DmxOWMWnSe6lhIsLA6r//\nVWAlItK0U2DcsASVCC/K1bQGxAUR4USzUcUuJIVT4VTqkUfyn3/84/HbNWoqmUILFpQObmspR1zS\n9korBe+51JhJUcuXBy2an/kM/OhH1ZclqTCQOf306l7/8Y/nDx1x773x2yXt+k6iWGD18MP5+XOh\n8A7HPfZItv/HH++eyxgKA6t77w3uUFVglX0aDymbVG/p0LS5AqtppYpT7qL/29927+Yqd9t8qNhU\nKlDfrsBq7tyrt2Lvp1yOVbn11QQncS1W4ZQ3pUY3r2aMqFLHvv326vZROGl2YQtWpWppsQLYbrvu\nQ3IUay0sZtw4uOee+HXReQo/+ECBVStQrk42qd7SoWmBVa0n36RjPB17bPeLcdIcq4ULi4+43RM5\nVsVaOqqRJEAqppbAqh6WL+8KFEoFVsuW9VwrX9r8979B8BOKBlZxPyRK5dTFKfW5RgOrpUsVWIlI\ne2taYFWv4QOSBEmFrWN/+lOyfS9dCltt1X35llvCq68m20ctjjuufvsqF3Ccdlr88hdfhAED4tdB\nYxLb41qswmWlAqvolC3V+tOfukaIj+ZzNTNge/HF8tsU3vxRrl4qfT/F9ueeH1gtWaLASkTaW9MC\nq8LukkrVMuBinEoGDV2woP4TR6dV4UTGPSEusAq704pNuQJBkF1roPfcc12PK+0uS5Ok41jVavny\n/NbOJUsaP+aaNJ7GQ8om1Vs6NC2wqlUzLvihSu/ik8oUBgVPPRXMERm3LqoeXYHF8oiy1sVYGDjN\nn196fTmlhrz4/e+7nqvFqjW0w4X5o49g6NBd6dWr/r8EttlmCI89VmJG9AZph3rLgswGVg891OwS\nSE8KW6yig48WuuYaGDmyscfPisJAqLA7t5ZpiqKWLs0fqV+BlWTFhx8+AjRikL5nWbTo5AbsV7Ii\ns4HVwoXNLoE0StxFPMldpKefHkzi3Qi1dl33tHp3BSYdpFXJ65Id6zZov+uU30Ramk6BkglJW4wa\nNfnvggU8JaxEAAAgAElEQVSN2W+jHHVU6fWVBoqlWqyi1GLVGjQeUjap3tIhsy1W0rriBg7NWldc\ns02fXnp9pXMg/u1v8csL60UtVq1BuTrZpHpLB50CJROSDuoqgVpnB0iqsCvw9dcVWIlIe9MpUDLh\nzTebXQKJs/HG3ZcpsBKRdlb2FGhmI8xslpm9YGZnlthuVzNbamaH17eIIpIlCqyyT7k62aR6S4eS\nOVZm1huYABwAvAw8amZT3H1mzHbjgTuBHpjsRUTSSgOEZp9ydbJJ9ZYO5X5b7gbMdvc57r4EuBGI\nGynoVOBmYFGdyyciGaMWKxFpZ+VOgf2BeZHn83PLVjCz/gTBVjiCUMbGqBaRelJgJSLtrNwpMEmQ\ndAkwxt2doBtQXYEibcx0Bsg85epkk+otHcqNY/UyEJ0MYwBBq1XUzsCNFpxNNwAONrMl7j6l++7G\nRh535P6JSOvo5IorOtlww2aXQ2qhXJ1sUr2lQ7nA6jFgsJkNAl4BjgDyxnR29y3Cx2Z2DXB7fFAF\n+YGV1GrLLRs30rhIdTo47bQOttoqeKZfzyLSbkoGVu6+1MxOAe4CegNXu/tMMzspt35iD5RRilCX\ni6SRcqxEpJ2VndLG3acCUwuWxQZU7n58ncqVKWuuCe+91/PHVWAV6NWr8kmFpXEUWGVf2NKorqVs\nUb2lg+YKrAPNYyfSJQ2BlZnNAd4BlgFL3H03M+sLTAY2A+YAo939rdz2ZwEn5LY/zd3vbka500IX\n5mxSvaVDCk6B2bftts0uQfrss48Gimx1AwfGL09JS6oDHe4+zN13yy0bA9zj7lsCf8k9x8yGEOSP\nDgFGAJebmc6NIlIVnTzqYGTckKk9ICUXMNZZJ35ZGloupHHmzo1fnqJ6L/wfchgwKfd4EjAq93gk\ncIO7L3H3OcBsgsGRRUQqlp5TYIbVEuB8/OP1K0ezhC1T0RYqs1RdYFPrvvuSb3vWWcm33a2JYUFK\n6t2Be83sMTP7am5ZP3dfmHu8EOiXe7wJ+cPIdBsIud1oPKRsUr2lQypOgcW6FLLEqxxv/hOfqP6Y\nPdliVepiGQZUG28cv7zRqv3s662jo/LXrL128m0vuCD5tnvuWXlZCp1+enWvS0lgtbe7DwMOBk42\ns32jK3MDGpf65qTkW9Uc5513nvJ1Mkj1lg6pSF5vZi7O1lvDrFm17aOWAGfZsuDvaqvBBx/UVo5m\nCetvpYJv06hRcP31PV+eZrnvPth//8paoeoRhKy9NrzzTv6yevyfOvRQ+PnPK39dGgIrd38193eR\nmd1K0LW30Mw2cvcFZrYx8Fpu88KBkDfNLcszduzYFY87OjroqCaSFpHU6uzspLOzs+b99Hhg9eqr\n3Vs2+vQpvv2gQTBnTuPK0+zWjjCwqmc57r8f9tuvfvsrp1hgddFF9Q2sOjog7jtv1th63Hjj4Hub\nRKVBdtIg5LTTiq/78Y/h5JNrK0ecavfR7MDKzFYHerv7u2a2BnAQMA6YAhwLjM/9vS33kinA9Wb2\nM4IuwMHAI4X7jQZWItJ6Cn8wVdut2uOnwI02gm98I3h85pkwfTrcXeTG5i9/Gb797caWpx4X5Hq0\nWNXzuKutVv0+Q1tvXfpYu+/e9Ti8kEZbST75yfpfYDfYIH55o4Pjxx8vvm7LLZPtY4014pcnbVlK\nMk7XGWd0Pa7HZ5/VwIogd+oBM5sOPAz8KTd8wk+AA83seWD/3HPcfQZwEzCDYMy+r+e6CtuWcnWy\nSfWWDk3pCvzYx7oeDx1afLtx42rvpgtddx0cfXT35c0+fYaB1SqrwH//W9lr4y58V14Zv+3kyXDE\nEdXvO/p8xozSLSgQXOQXLUp+vHHjIK2pAaWCn5NP7vqhUI2kQUipADysm2226b6sFtUGSM0OrNz9\nX8COMcv/AxxQ5DUXABVksbU25elkk+otHZpyCkxy0j/9dNh8czj44GBU82K/+Kvdf6ieLVYjRsSv\n/+lPi782vGBOm5a/fKONkh836vOfj9/2858PAqLNNiu/X+jeQhI91rJlyVpQKqmHc89Nvm0tCf/V\naGQOYLEg5JRT8p+X+rwLP+dvf7s+wU2SfcS1jjY7sBIRaaamnAKTBDPRi9kaa8C+kXt6Vl+99GvX\nXbf7smIX+Xq2WE2d2n3ZG2/Ad75T/DVhYLX55vUpQ/Si9r//2/W4d++gRaNv39KvP/74YB+lPpdN\nNsm/0IfbFr4m7RfYffZJtl34PuIC58Lv1f/9X7J9fvKTcPPN8Z/R88/DL36Rv2yLLbpvF1p55fyy\nfO1rPZdjtXhx92Vpr3cRkUZK1SkwGlwUthJET/KVnLjD1x16aPz6uJaAAbn7g844A+bNCxLo4wbB\njCtboXKBTC05VnF69eoqz4EHBn+j3anlPruJE4M7zEq1WPXtC5dd1vW8WGtKvS+wYRnqFQxffz3s\nskv8ulVX7X7cP/+5/D6/8IX45Xvtlf98n32CVsS4787gwfnP11+/eHB+1VXwpS/lL1u+vH4tVn/5\nS3Wvk2xTrk42qd7SoamnwMILZPR5qe4Xs/h8qej6UHinWvirPhQGcXFBQXhhuPBC2HRTmD0bXu52\n83V9bLhh/PIkrQVx20QvauH6aAtfqYveOusEd2iusUbpwApg++27WnDWXDN5+Uo566zg86636F3x\nha2dxYK0mTO7Hq+ySnCXZdz7Sfoeb745fnmS16++evF6Gziwe4sVwLHHdh+HqtKxrcyqa/lKy4wA\nUj2Nh5RNqrd0KBtYmdkIM5tlZi+Y2Zkx60ea2ZNmNs3MHjWzvZMevDDfJ3qRK/er99prS5W5/LKd\nd+5+zGLH7t27dI5XJXMFrrMOfOpTXc8nT84P2oYNgyefLL+fIUPilxcGVj/7WdB1F7e+0CuvdD0u\nF1hF9e8Pb7/d9Vmut175Y0UddFDw94ILSg+CGZah0gv3XXd1PY62REFXmQvLutlmXWVZaaX4YR4q\n0ZPBhnvQ6lX4WRa2uoY/OvbdF3bsluqd3/pZCbVYiUg7K3kKNLPewASCiUmHAEeZ2TYFm93r7kNz\noxyfAFxVbH/hBTe8mEVzgKLLofvJOVx3zTXw61+XKnXhe8j/W7i/JIFVKZ/5DHz2s8m333pruOee\n4PFddwUXu2jg06cP7LBD1/P99guGpAgNHAgLFwbJ7uVarCBotYi2/hV7byeemN+aU0l3m1n+COJh\n+ZNelKOBTyNE3/+AAUEL24ABQRdbKFrWJ54Inoc3ApQK6HoiYCo8RrmZCooFi4XTJ0X3++lPd9+P\nAisRkcqVOwXuBsx29znuvgS4kWDC0hXc/f3I0zWBovcvnX9+wcGLBE+lHHdc8TyWOMUuisOGFT9m\nsYtJ2J0THeC02LZxCfQQBC/ha5LcoTdwYP6QFAceGAxXUdi1GYrrCoxKmsQfbbEaMwa+973ur/nV\nr+L3deed3cuSVDUX8nLfm2g5OjvhpZfg3/8O6uKWW7ofN/xuhK+rJXgqdndnJfss3Pbgg0tv369f\n/OuKjQnnHrRuvfRSMHJ89LgKrNqTcnWySfWWDuVOgf2BeZHnsZOTmtkoM5sJ/Img1SpWYQJvoXol\nJUcvZnfeGbSIRE/2/fp1BQpxxyyW37XddsHfaJfZNoXtdznFvtuTJ8cvDxUGgtHy7b03XH556deX\nC6xOPLH7yPdxosf98Y/hhJhaDZP8C48Tdrc1M9cmOlJ6WI7vfS8IeKN5bWFwW6r1r5ZAYa21gr/h\n5zl+fPHjAVxySfdlhdtGu6WjN3yEI9CHLXGFrwu/14WtU+7Be9x8866gGIJlW20VX85SFFhln3J1\nskn1lg7lToGJQh13v83dtwFGAT8stt1OOwV/99ij64JTTLUX5d69gyldQh0dQQ5PdH8rr1z6DrNy\nd/KFHn+8eE5QYflXWSX4G72ol3qPxx7bfdnaa+e3VEVfH+bPlOu+OeEEuPHG4utDleRYFVsXl6sG\n3adfidpnn6DO5s8PAslqrb9+8Ty0OHEtS0lyusr9GLj3Xnjmma7tqhkTq/D44c0C7vljepUa1BWC\n+vjf/+3qSox7X336dI2hZdbV+nX88UGwGj4vRYGViLSzciOvF05OOoCg1SqWuz9gZluYWd/cKMd5\nonNtTZnSAXQUvL7rcbWB1b77dv1ij14sw/1NnZo/XUt4zOgkyEkvDKutVvxCWSynq9Q2URdcELQU\nlRJ9/YMPBoFEdFk0Sb7ccUt1BZZTLEgtdqGfMCFodbnwwu77GjSoaxLjqVPzc7fGjAmm0rniivJl\n6tMHpkxJNpioezCu1Lx5+cvjPqdbb4XPfS7/taWEQUw43lN412MlA94Wiubghfbcs3uLcFxgdcUV\nXaPmF6u38Hn0sz/88OD/U/lW5U7Gjesst5GISMsqF1g9Bgw2s0HAK8ARwFHRDczs48BL7u5mthOw\nclxQBeUnMa02sPrnP7suKtGxhqJTjYT7GzIkP/m3ku7HcoFD6Cc/qT6wKtfqkGTd9tvDpZfWdvFu\nRItVpcFyXF7csGHdA6uwCwyCAOlvfwseb7ZZ/rAcSY5/xx2ltx81Kvi7ww7w1FPJvz+rrx50IW+0\nUfDacMDPaPBSyL17nlN4vMLhP/75z+6vL/ZdCv8OHAgvvND9dUccAc891z3ZPXr84joYO7ZjxTPl\ne2RTWG/qVsoW1Vs6lAys3H2pmZ0C3AX0Bq5295lmdlJu/UTg88AxZrYE+IAg+KpKqZN2qXXR28jL\njcpeKK5lpti4TFE33BA/Ae+FFwYjX5caDiJUaaBR6jNYZZWubsmVVy4/l18txyoUvo9f/CJICi9c\nHmpUF1G0rNH6X2mlZPUQVS4xPHTmmUHQFvf9mT49GM6jcPDXMLctmpe34YZw221dAVucuO9J9E7S\npK8rfP7ww8F0S9EJtSFo9Q3vXC18rcaoag+6MGeT6i0dyk7C7O5TCWZ8jy6bGHl8IRDTqVO59dfv\nSjpO0uLz4INBF0iSvI9iwv2usUZXV+C4cXDRRcW3BTjyyPj9nXFG8DdJ+eNUcuEq3OdZZ1V/jHJd\ngUlarAqnfAkDqZkzg2Ci0sCq2YNTJnnPcbmCQ4cG3cTvvZfsOOW+G/UOZsL9rbde6XHD4l5TSRex\niEg7Sk2a6fz5Xbk1Se2xR3CLeHQ8okqFF7UJE7qWrblm1x2A1aq2KzBuUtti20dbRKppYSqlnpNT\nhy01W23V1Rp4+unw1a/Wfoxix2z0vsxg7twg96jWcpQLVuodWH3rW8FUOJUodbPHK6+U7tIUEWkn\nqQms+veHDTboel54MSkWPJWavDhJcBBuU2ouwFAld3RVE1g9+2yyO/ZCcRMhVyscDDNu3+WUu/CH\n6//4x67coI02gv/5n+T7Peqo4ttFlfocas3xKlw3cGDQ6hOXhF9Ly2M9XXpp92UDBwbDbtTLxht3\njeIurUHjIWWT6i0dUnc6vPbaYFLZj30sf3mplpxikiRvRy9ql14aJLwXuygOHhx0PyZRuI+f/hSW\nLi39mnLDAxRegKttsYrzmc+UPlYorlWxXBARXnRXX736lo3rr6/udUnVEnTV0hUN+Z91/26jxNVv\n37VQblV7Ua5ONqne0iF1gVV4O/pXvlLbfmbMSH6rffj3tNNKB1ZmQfdjEoX7+Na3ym9TqWigVs8W\nplL7i05onGR/7l3lrOSOxyTrIchxGjkyKG+54KvS8aiquROyGuGx33kn/saJeh2rlv2Er11jDXjj\njfqUR0SkFaWmKzAUXtBrvVV/m22CsYyqkaZf56XKUm1XYDhnYyn1TFKudmqYpNv/7ndw3XXVHaPa\n40fXJRk2o5Tws15rrdr3VUqld8zGlSHJd0dEpJ2lLrBqZL4JdL9INeoupyQXw0qTyAunool2BVZy\n0dxmG/jrX0tvU/i51JK7lNVb9TfaqPhgpOUCq0r0xF2Bt95afraDJIqVNWt1K6UpVyebVG/pkLqu\nwEYHVkmOV4+LRJJ9VJLwu3Bh9wtjmK/05pvFJ30uplyyfj3vMiy2fvfd4ec/r/x1F18c3JF32mmN\n/b6EU8BUUrZQJa2lPX1XYKXOOScY1gR6/v+nNIdydbJJ9ZYObRdYFV7w4i5qPRFYPfxwZYnKhcn8\nEAzi+MEHlQdVSdQ7Zwu61+2qq8I3v5n8OKHDDgv+xg2Ceu65xQfbbFQ3Ydx+H34YPvww2b5KfecH\nDOg+gGc1annv55/f9VjjWImIlJa6wKrYiXv4cLjzzuKv++9/uyY6Lmb27O6T7a61VveBHHsisNpt\nt9r3ExdsJVWufPUeF6sa4X5vuSXZ9iNGwC67FA9EGhW0x73/cMqaJEaOhF/9Kn7dv/5Vn8+3Xu+9\n3H6ef74+xxERyarU5VhF5/GLOvroYBDRYsoFVdB97rMXXojv6umprsAkBgwov001yg3t8I9/1P+Y\n1V7cDzkkfvlWW8GOO3Y9nzq1+Fhjd98N3/528WNUWl/1nJ5nzTWLD5bau3dtx6p3MFkux2rw4Poe\nT5pDuTrZpHpLh9S1WG29dc/lcXziE/F5TmkJrN54o3EjWpcLRHfdNdl+Pv7xoJUoiUrrNfwMiwUW\n06cn/5wPPLCyY5dTz+T1Rgo/83qVMWxRHj48f3maPwOpnHJ1skn1lg6pC6x6WjOT18vp27f2fTTa\n7NnJt602YC4WWK26anX7q4dowJuFoKJeP1bCwKrcXaUiIu2q7QOrRnVdZOFiG3r22cYfY+rU/CmL\nkkjrMA1z5+Z30aatfI2kuwJFREpLlL1hZiPMbJaZvWBmZ8asP9rMnjSzp8zsH2a2Q/2L2hhf/CLs\nsAMMHdq1rJ0ulFD7lCxJjBhR/eeatvoYODB9ZSqnXuUtFlhprsDWolydbFK9pUPZ06GZ9QYmAAcA\nLwOPmtkUd58Z2ewl4JPu/raZjQB+BSSc/KW5zODJJ7svq8d+pT7S/lmmvXz1VOyu3aFD4dVXe7Ys\n0jjK1ckm1Vs6JGmx2g2Y7e5z3H0JcCMwMrqBuz/o7m/nnj4MbFrfYvasJHcYltNOF9tGWWklmDmz\n/HbNlua6DluYit1ZWe3+CqX5MxAR6UlJAqv+wLzI8/m5ZcWcCNxRS6Gaae7c7lPHVKMVLjTuzc+p\n2XrrnjlO3OTHraTaeTMLaYBQEZHSkmRGJL60mtlw4ARg77j1Y8eOXfG4o6ODjo6OpLvuMcXG0apU\nKwRW7eR3v4PXX6/ute1U1+UC7c7OTjo7O3ukLNI4YZ6OupayRfWWDkkCq5eB6DCVAwharfLkEtav\nBEa4+5txO4oGVq2uHq1e0nP69q1+eIt2Cqx++EOYN6/4+sIfTEqkzSZdmLNJ9ZYOSQKrx4DBZjYI\neAU4AjgquoGZDQRuAb7k7hWMbNS6hg+Hd95pdimSaXZ3n2THMcfEL2+n4FJEpJSyOVbuvhQ4BbgL\nmAFMdveZZnaSmZ2U2+xcYD3gCjObZmaPNKzEGbLWWs0uQWmnntrsErQGBRXp/66LiPSURONYuftU\nd9/K3T/h7j/OLZvo7hNzj7/i7uu7+7Dcv4RTDEszXXZZs0vQGgon9k6TnmqNnDgRnnuuZ44ljafx\nkLJJ9ZYOGtZPElljjWaXIL322iu93b5f/Spsvnnjj7POOsE/aQ3K1ckm1Vs6JGqxEll7beVilZLW\nrrC114bDD292KURE2ocCKxEREZE6UWAlaokSkTzK1ckm1Vs6KMdKRFpObo7Tx4D57v5ZM+sLTAY2\nA+YAo939rdy2ZxEMbLwMOM3d725OqdNDuTrZpHpLB7VYiUgr+gbB8DBhe+wY4B533xL4S+45ZjaE\nYGy+IcAI4HIz03lRRKqmE4ioK1BaipltChwCXAWEo4wdBkzKPZ4EjMo9Hgnc4O5L3H0OMJtg4nkR\nkaoosBKRVvNz4AwgOmV0P3dfmHu8EOiXe7wJ+VN0lZtkvi0oVyebVG/poBwrEWkZZvYZ4DV3n2Zm\nHXHbuLubWal22th1WZhEvl6Uq5NNqrfa1GsSeQVWItJK9gIOM7NDgFWBtc3sd8BCM9vI3ReY2cbA\na7ntCyeZ3zS3rJt2mkRepB3VaxJ5dQWKcqykZbj799x9gLtvDhwJ/NXdvwxMAY7NbXYscFvu8RTg\nSDNb2cw2BwYDmutURKqmFisRaWXhz4afADeZ2YnkhlsAcPcZZnYTwR2ES4Gvu+unRvhLXV1L2aJ6\nSwdLcg4xsxHAJUBv4Cp3H1+wfmvgGmAYcLa7XxyzD52vUsgMFiyAfv3KbytSKTPD3a38lumm81dz\njBlzNuPHrw6cXeOewq9gT9ThUwwc+CXmzn2qB44ljVTt+atsV2BuoL0JBGO8DAGOMrNtCjZ7AzgV\nuKjSArSDeiTDZVk7v/92fu8iIu0oSY7VbsBsd5/j7kuAGwnGflnB3Re5+2PAkgaUMfPSfnHt3bux\n+0/7+2+kdn7vIiLtKElg1R+YF3mucV5ayAsvwAYbNLsUIpImGg8pm1Rv6ZAkeV2JBS3sE59odglE\nSjOz3xKMjj612WVpF0p+zibVWzokCawKx3kZQP5IxYmZZT6HtWrt/iuind9/O7/3OvkqcISZTQb+\nSXADzftNLpOISKwkgdVjwGAzGwS8QjBh6VFFti0aObXCnUEi0hTrA1sAbxNMR/NrgvOQiEjqlA2s\n3H2pmZ0C3EUw3MLV7j7TzE7KrZ9oZhsBjwJrA8vN7BvAEHd/r4FlF5H28G3gcnd/EcDM5pXZXmqk\n8ZCySfWWDokGCM3lNkwtWDYx8ngB+d2FIiL10hkJqg519z83u0CtThfmbFK9pUPDp7QxsxFmNsvM\nXjCzMxt9vGYxszlm9pSZTTOzR3LL+prZPWb2vJndbWbrRrY/K/eZzDKzg5pX8sqZ2a/NbKGZPR1Z\nVvF7NbOdzezp3LpLe/p9VKPIex9rZvNzdT/NzA6OrGuZ9w5gZgPM7D4ze9bMnjGz03LLG1n/+0Ue\n71v/dyUiUj8NDawSDi7aKhzocPdh7r5bbtkY4B533xL4S+45ZjaEIEdkCMFnc7mZZWnexmsIyh1V\nyXsN8+2uAE5098EEeXyF+0yjuPfuwM9ydT8svHutBd87BGPVne7u2wJ7ACfn/k83sv43NLNPmdn+\ngOYIEJFUa/TFvOzgoi2mMEH/MGBS7vEkYFTu8UiC28eXuPscYDbBZ5UJ7v4A8GbB4kre6+5mtjGw\nlruHE97+NvKa1Cry3iH+xo2Weu8QdPu7+/Tc4/eAmQTj2jWy/k8DtgS2Br5Z33ckcTQeUjap3tKh\n0ZMwxw0uunuDj9ksDtxrZsuAie5+JdDP3Rfm1i+k69f2JsBDkde2wqCrlb7XJeQP2/Ey2f4MTjWz\nYwjuov22u79Fi7/33J3Cw4CHaWz9DwTWAVYBvgGcX3vppRTl6mST6i0dGh1YtdPgonu7+6tmtiFw\nj5nNiq50dzezUp9Hy3xWCd5rq7mCrov9D4CLgRObV5zGM7M1gT8A33D3d6Nj1DWg/r9F8JlqyiwR\nSb1GdwXWbXDRtHP3V3N/FwG3EnTtLcwNRUGu6+O13OaFn8umuWVZVsl7nZ9bvmnB8kx+Bu7+mucA\nV9HVrduS793M+hAEVb9z99tyixtZ/8+4+zPu/py7P1entyEi0hCNDqxWDC5qZisTJLFOafAxe5yZ\nrW5ma+UerwEcBDxN8F6PzW12LBBehKYAR5rZyma2OTAYeIRsq+i95oboeMfMds8lM3858ppMyQUS\noc8R1D204HvPlfdqYIa7XxJZ1cj6H25mt5vZ783s9/V+T9KdcnWySfWWDg3tCiw2uGgjj9kk/YBb\nc90hKwHXufvdZvYYcJOZnQjMAUYDuPsMM7sJmAEsBb6ea+3IBDO7geAW+A0sGKzxXOAnVP5evw78\nBlgNuMPd7+zJ91GNmPd+HtBhZjsSdOf+CwgHz22p956zN/Al4Ckzm5ZbdhaNrf8jgW3c/VEz27TI\nNlJHytXJJtVbOliGruci0obM7ErgI3c/2cwud/evN6EMWfrt0zLGjDmb8eNXB86ucU9hDmBP1OFT\nDBz4JebOfaoHjiWNZGZVTcfX6OR1EZFavUfXEBcfNLMgIiLlKLASkbR7HdjXzC4Glje7MO2gkjnn\nlixZwqJFixpSjvfeexdYvSH7bkWaKzAd1BUoIqlnZlsDvdx9RpOOr67AIqZNm8Yuu+zKqqt+rCH7\nX7z4TILhy2qhrkCpnLoCRaQl5W4YAFgtd6LLxCj17WTNNbfnnXemld9QpA0osBKRVHP3o2DFUA+n\nN7k4IiIlKbASkVQzs20J+nD6ANs2uThtQbk62aR6SwcFViKSdl/I/f0QuKyZBWkXujBnk+otHRRY\niUjaPRZ5vKmZberuf25aaURESlBgJSJp9xXgHwTdgfuQkel/RKQ9KbASkbSb5e4XAZjZhu4+qdkF\nanXK1ckm1Vs6KLASkdQzs6sJWqwWNrss7UAX5mxSvaWDAisRSbuzgU2BtwgS2EVEUqtXswsgIlLG\nJcB57v4O8ItmF0ZEpBQFViKSdsuBubnHbzWzIO1i3LhxK/J1JDtUb+mgrkARSbsPgSFmdiqwXrML\n0w6Uq5NNqrd0UGAlIqmVm8bmZmADgpl0L29uiURESlNgJSKp5e5uZsPd/cJml0VEJAkFViKSWmY2\nEhhpZp8G/gPg7l9sbqlan8ZDyibVWzr0WGBlZt5TxxKR9HB3q+HlI9x9bzO7wt3/r26FkpJ0Yc4m\n1Vs69Ohdge7eEv/OO++8ppdB70PvJQv/6mCgmR2a+3uImR1Sj52KiDSKugJFJM1+T5C4fhOwYZPL\nIiJSlgIrEUktd/9Ns8vQjpSrk02qt3RQYFWFjo6OZhehLlrlfYDei3Qxs1WB+4FVCM5xN7v7WDPr\nC0wGNgPmAKPd/a3ca84CTgCWAae5+93NKHta6MKcTaq3dLA65UGUP5CZ99SxRCQdzAyvLXm92uOu\n7u6LzWwl4O/AN4DPA6+7+4VmdiawnruPMbMhwPXArkB/4F5gS3dfHtmfzl9FTJs2jY6OE3jnnWnN\nLmLtpWEAABydSURBVEoJ4VewJ+rwKQYO/BJz5z7VA8eSRqr2/KUpbUSk5bj74tzDlYE+BFfUw4BJ\nueWTgFG5xyOBG9x9ibvPAWYDu/VcaUWklZQNrMzs12a20MyeLrHNZWb2gpk9aWbD6ltEEZHKmFkv\nM5sOLATudvdHgH7uvjC3yUKgX+7xJsD8yMvnE7RctS3NOZdNqrd0SJJjdQ3BjPK/jVuZu/35E+4+\n2Mx2B64A9qhfEUVEKpPrxtvRzNYBbjWz7QrWe5mx9bqtGzt27IrHHR0dLZ0Lp1ydbFK91aazs5PO\nzs6a91M2sHL3B8xsUIlNVjSvu/vDZraumUV/GYqINIW7v21m9wGfBhaa2UbuvsDMNgZey232MjAg\n8rJNc8vyRAMrEWk9hT+Yqm39q0eOVX9gXuT5fIITk4hIjzOzDcxs3dzj1YADgZnAFODY3GbHArfl\nHk8BjjSzlc1sc2Aw8EjPllpEWkW9hlsozJrX7TMi0iwbA5PMrDfBj8fJ7n6HmT0E3GRmJ5IbbgHA\n3WeY2U3ADGAp8PV2vwVQ4yFlk+otHeoRWCVqRof2ylEQaUf1ylGohbs/DewUs/w/wAFFXnMBcEGD\ni5YZujBnk+otHRKNY5XLsbrd3bePWXcIcIq7H2JmewCXuHu35HWNAyPSfpo1jlW96fxVnMaxKqRx\nrFpFteevsi1WZnYDsB+wgZnNA84jGBcGd5+Ya2I/xMxmA+8Dx1daCBEREZFWkOSuwKMSbHNKfYoj\nIiLNplydbFK9pYPmChQRkTy6MGeT6i0d2mpKm+XLl+c9V86EiIiI1FNqAqtRo0axYMECAK6++mom\nTpyYt/6iiy5i+PDh7Lzzztx7770AzJ49m0996lMMHz6cM844A4Cf/exn7LXXXuy7775MmxYkU+60\n005885vf5JhjjmHcuHEcd9xxHHrooTz1lJILRUREpI7cvUf+BYcq7tprr/VLL73U3d0POeQQf+ON\nN/LWL1682N3dFy5c6Pvtt5+7u3/uc5/zJ554wt3dly9f7q+++qp/8pOfdHf3OXPm+IEHHuju7ptv\nvrm/+OKL7u4+duxYP/fcc0uWRUTqI/f/vsfOM436V+781WrGjh3rY8eOTbTtE0884WuvvaODp/gf\nuX89cawnfeDA7RtcQ/EqqTcpr9rzV2pyrEaOHMmoUaM4+uij6dWrF3379s1b/9vf/pbrr7+eXr16\nrWjZmj9/PsOGBXM+mxlz585l6NChAGy22Wa89dZbAKy33npsscUWK/a166679sRbEhHJJOXqZJPq\nLR1S0xW45pprsv7663PxxRfzxS9+sdv6CRMm0NnZyY033rgiV2rAgAEruvvcnUGDBjF9+nTcnTlz\n5rDeeusB0KtX/ts0y/ywOiIiIpJCqWmxAhg9ejTHHXccr7zySrd1++yzD3vvvTd77LEHa621FgAX\nXnghX/3qV3F3dtllF376058ycuRI9tprL3r16sWECRNij6PASkRERBoh0cjrdTmQRi4WaTsaeT2b\nKhkPSSOvF2reyOsax6q+GjbyuoiItBddmLNJ9ZYOqcmxEhEREck6BVYiIiIidaLASkRE8owbN25F\nvo5kh+otHZRjJSIieZSrk02qt3RQi5WIiIhInSiwEhEREakTBVYiIpJHuTrZpHpLBw0QKiINowFC\nW58GCC30FCuttDubb75dQ/b+3e+exFe+8pWG7FvyNWyAUDMbAVwC9AaucvfxBevXAa4FBuT2d5G7\n/6bSgoiIiGTfJ1i69G+88EIj9v3/ePXVVxuxY6mjkoGVmfUGJgAHAC8Dj5rZFHefGdnsZOAZd/+s\nmW0APGdm17r70oaVWkREJJVWB3Zt0L6nNGi/Uk/lcqx2A2a7+xx3XwLcCIws2GY5sHbu8drAGwqq\nRESyS7k62aR6S4dyXYH9gXmR5/OB3Qu2mQDcbmavAGsBo+tXPBER6WkaDymbVG/pUK7FKkmm3wjg\nCXffBNgR+KWZrVVzyUREREQyplyL1csESemhAQStVlHHAT8GcPcXzexfwFbAY4U7Gzt27IrHHR0d\ndHR0VFpeEUmxzs5OOjs7m10MEZGmKRdYPQYMNrNBwCvAEcBRBdv8myC5/R9m1o8gqHopbmfRwEpE\nWk/hDyble2RTWG/qWsoW1Vs6lAys3H2pmZ0C3EUw3MLV7j7TzE7KrZ8I/AD4jZk9RTBYyHfd/T8N\nLreIiDSILszZpHpLh7LjWLn7VGBqwbKJkcevAp+uf9FEREREskVT2oiIiIjUiQIrERHJo/GQskn1\nlg5luwJFRKS9KFcnm1Rv6aAWKxEREZE6UWAlIiIiUicKrEREJI9ydbJJ9ZYOyrESEZE8ytXJJtVb\nOqjFSkRERKROFFiJiIiI1IkCKxFpKWY2wMzuM7NnzewZMzstt7yvmd1jZs+b2d1mtm7kNWeZ2Qtm\nNsvMDmpe6dNBuTrZpHpLB+VYiUirWQKc7u7TzWxN4HEzuwc4HrjH3S80szOBMcAYMxtCMMH8EKA/\ncK+Zbenuy5v1BppNuTrZpHpLB7VYiUhLcfcF7j499/g9YCZBwHQYMCm32SRgVO7xSOAGd1/i7nOA\n2cBuPVpoEWkZCqxEpGWZ2SBgGPAw0M/dF+ZWLQT65R5vAsyPvGw+QSAmIlIxBVYi0pJy3YB/AL7h\n7u9G17m7A17i5aXWtTzl6mST6i0dlGMlIi3HzPoQBFW/c/fbcosXmtlG7r7AzDYGXsstfxkYEHn5\nprllecaOHbvicUdHBx0dHQ0oeTooVyebVG+16ezspLOzs+b9KLASkZZiZgZcDcxw90siq6YAxwLj\nc39viyy/3sx+RtAFOBh4pHC/0cBKRFpP4Q+malv/ynYFmtmI3C3IL+TupInbpsPMpuVube6sqiQi\nIvWxN/AlYHjuvDTNzEYAPwEONLPngf1zz3H3GcBNwAxgKvD1XFehiEjFSrZYmVlvYAJwAEHT+KNm\nNsXdZ0a2WRf4JfBpd59vZhs0ssAiIqW4+98p/qPxgCKvuQC4oGGFypjwl7q6lrJF9ZYO5boCdwNm\n525BxsxuJLg1eWZkm/8B/uDu8wHc/fUGlFNERHqILszZpHpLh3Jdgf2BeZHncbchDwb65kY6fszM\nvlzPAoqIiIhkRbkWqyR5Bn2AnYBPAasDD5rZQ+7+Qq2FExEREcmScoFV4W3IA8gfSA+CFq3X3f0D\n4AMz+xswFOgWWLXT7coi7ahetytLcylXJ5tUb+lgpW5+MbOVgOcIWqNeIbgF+aiC5PWtCRLcPw2s\nQjDC8RG5O22i+9KNNiJtxsxwd2t2OWql81dx06ZNo6PjBN55Z1qzi1JC+BXMeh2ew/nnr8w555zT\n7IK0hWrPXyVbrNx9qZmdAtwF9AaudveZZnZSbv1Ed59lZncCTwHLgSsLgyoRERGRdlB2gFB3n0ow\ntkt02cSC5xcBF9W3aCIiIiLZorkCRUQkj+acyybVWzpoShsREcmj5OdsUr2lg1qsREREROpEgZWI\niIhInSiwEhGRPMrVySbVWzoox0pERPIoVyebVG/poBYrERERkTpRYCUiIiJSJwqsREQkj3J1skn1\nlg7KsRIRkTzK1ckm1Vs6qMVKREREpE4UWImIiIjUiQIrERHJo1ydbFK9pYNyrEREJI9ydbJJ9ZYO\narESERERqRMFViIiIiJ1osBKRETyKFcnm1Rv6VA2x8rMRgCXAL2Bq9x9fJHtdgUeBEa7+y11LaWI\niPQY5epkk+otHUq2WJlZb2ACMAIYAhxlZtsU2W48cCdgDSiniIiISOqV6wrcDZjt7nPcfQlwIzAy\nZrtTgZuBRXUun4iIiEhmlAus+gPzIs/n55atYGb9CYKtK3KLvG6lExGRHqdcnWxSvaVDuRyrJEHS\nJcAYd3czM0p0BY4dO3bF446ODjo6OhLsXkSyorOzk87OzmYXQ2qkXJ1sUr2lQ7nA6mVgQOT5AIJW\nq6idgRuDmIoNgIPNbIm7TyncWTSwEpHWU/iDSb+eRaTdlAusHgMGm9kg4BXgCOCo6AbuvkX42Myu\nAW6PC6pEREREWl3JwMrdl5rZKcBdBMMtXO3uM83spNz6iT1QRhER6UFhS6O6lrJF9ZYOZcexcvep\nwNSCZbEBlbsfX6dyiYhIk+jCnE2qt3TQyOsiIiIidaLASkRERKROFFiJiEgejYeUTaq3dCibYyUi\nIu1FuTrZpHpLB7VYiYiIiNSJAisRERGROlFgJSItxcx+bWYLzezpyLK+ZnaPmT1vZneb2bqRdWeZ\n2QtmNsvMDmpOqdNFuTrZpHpLB+VYiUiruQb4BfDbyLIxwD3ufqGZnZl7PsbMhhDMKDGEYIL5e81s\nS3df3tOFThPl6mST6i0d1GIlIi3F3R8A3ixYfBgwKfd4EjAq93gkcIO7L3H3OcBsYLeeKKeItCYF\nViLSDvq5+8Lc44VAv9zjTcifWH4+QcuViEhVFFiJSFtxdwe81CY9VZa0Uq5ONqne0kE5ViLSDhaa\n2UbuvsDMNgZeyy1/GRgQ2W7T3LJuxo4du+JxR0cHHR0djSlpg5x77vksWPB6Ra/52tdOK7vN66+/\nhrd9KJoOyrGqTWdnJ52dnTXvx7yH/keYmffUsUQkHcwMd7cmHHcQcLu7b597fiHwhruPN7MxwLru\nHiavX0+QV9UfuBf4ROHJqhXOX/36fYLXXjsK2LABe98QOKoB+62X8CuY7TqEczj//JU555xzml2Q\ntlDt+UstViLSUszsBmA/YAMzmwecC/wEuMnMTgTmAKMB3H2Gmd0EzACWAl/PfARV0rHAJ5pdCJGW\npsBKRFqKuxdrOjmgyPYXABc0rkTZM3bsuNxfdS1lSZhfpS7B5lJgJSIieRRQZZMCqnRIdFegmY3I\njUr8Qm5wvcL1R5vZ/2/v/mMtKes7jr8/3V1WcH9BiovCmkVL3SUBFOmC+AsqqSsoGG0iWIwWTTdN\nrabsVhbbZq8piW0TrKkoaxFNY6o0QcOvgiLBG1sKLHR/IneR5YfsCly2IGilyJL99o+ZK7Nnz7ln\n5pyZOzPnfl7J5J6Z85xnvs/M3Oc8Z+aZZ7ZJ2i7pDkknlh+qmZmZWbP1bVhJmgNcAawmGZ34Akkr\nO5I9DLwjIk4E/hb457IDNTMzM2u6PGesVgG7IuLRiNgHXEMyWvFvRMSdEfFcOns3yS3LZmbWQmNj\nn/tNPytrD49j1Qx5+lgdDezOzO8BTp0m/ceBm4cJyszM6uM+Vu3kPlbNkKdhlfvWY0lnAhcBbx04\nIjMzM7OWytOw6hyZeBkHPlsLgLTD+lXA6ojofAAq0P6Ri81semWNXGxm1lZ9R16XNBd4AHgX8Diw\nCbggIiYyaV4L3A5cGBF39chntMfdM7OD1DXyetlGof5KRl7/HnkGCB29caxmx8jrHseqXJWNvB4R\nL0n6JPB9YA5wdURMSFqTvv9VkpGNDweulASwLyJWFQ3GzMzqNzoNqtnFDapmyDVAaETcAtzSseyr\nmdefAD5RbmhmZmZm7ZJrgFAzMzMz688NKzMzO4DHsWonj2PVDH5WoJmZHcB9rNrJfayawWeszMzM\nzErihpWZmZlZSdywMjOzA7iPVTu5j1UzuI+VmZkdwH2s2sl9rJrBZ6zMzMzMSuIzVmZmZi1x/fX/\nzu7de0vPd84cGBv7K5YuXVp63rNN32cFlraiEXjWlpkV42cFNoefFQjtf1bgncA9Pd8dG/t5+vfw\nwjnPn38ZW7f+iBUrVgwa3MgZtP5yw8rMKuOGVXMUaViNnlFpWFVn4cIVbNp0nRtWGYPWX+5jZWZm\nZlYSN6zMzMzMSuKGlZmZHcDjWLWT91szuI/VEMbGxhgbG6s7DLPGch+r5nAfK3Afq97cx+pg7mNV\nA49wa20wTOPfPxzMzIrp27CStFrSTkkPSrqkR5p/St/fJulN5Ydp1i5NapAM8wPAPx7MzIqZtmEl\naQ5wBbAaOB64QNLKjjRnA78TEccBfwJcOV2eg3zhNOlLyqo30/u7ivW5QWJt5r467eT91hAR0XMC\n3gJ8LzO/HljfkWYj8KHM/E5gaZe8Iu2kEEUN8pm8NmzYMPBnq4wrayrGfrF2e79o+YbZHmXl3Wu7\nVhVbFfuxqmNjkG0wTCzDliP9/LT1TBummfpfr9KrXvX6gAcDYhZOpFPdcTR3WrjwDTExMVH3Ydoo\ng9Zf078JfwhclZm/EPhSR5obgdMz87cBb+6SVzbQQQpXmuyXUzbvvF9aU+nKiqvfevNuu27vF42x\nyi+QvHn3SldVbIPkm3efFflMHjP9/1Pks93K54ZVc7hh5YbVdJMbVgcbtP7q18cq8pz14uVbLop+\nrrCpyzbDXL7pdZmm2/Ju6yv7Ms8oXTaaict4/dbR7f28ceVNl91n032m33EzTFyDbIdh9MtvlI5j\nM7NBTTvcgqTTgLGIWJ3OXwrsj4i/z6TZCIxHxDXp/E7gnREx2ZFXwIbMkjPSycxGx3g6Tfkc4eEW\ncnviiSfYu7f8B+wCnHnmOTzzzA/xswJH1zD7zcMtHGzg4WKmO50FzAUeApYDhwBbgZUdac4Gbk5f\nnwbc1SOvPKfcIpuu6KW5bsuy+fWLoeh6u30m+9kqY+i2vfKkz+bd65Jot2Xd1jf1usp+P/3STXdZ\nNu86+q0376XfYfqQdTtuqtiu0+U9SPz9ysKAp9KbNg1zLBWxbt0lMX/+0li06ITSp0MPPSHgp7Vf\ncqpn8qXAfpMvBR5s0PqrfwJ4D/AAsAu4NF22BliTSXNF+v424OQe+UxbgLL7LU0ZpFFTtkEai3nz\nHLZD+JR+X7Td1jfM9hym0dxNvy/4osq4EaBI3mWpMu8pRY5nN6yKWbv2koDP1/4lO3qTG1b9Jjes\nDjZo/dW4kdfLHs08PZV30OuZVMV6i+Y5bAzdPl/X9uymSbGMuiL/ox55vZh169Zz+eVLSG7AtvLM\njkuBw/ClwIMNWn/NrSKYYVTZ+XnDhg2V5T3TipalirI3aXs2KZZR53HlRt/o9bGaHYbdbzfddBOb\nN28uMyQAFi9ezDnnnFN6vk3VuDNWFay39jMZo/BMwVEog808n7EqxmesquIzVv3Mm7eW+fOfKD3f\n/fuf5cgjH+PRR+8rPe+qjcwZq1E0Cg2SUSiDmZl1t2/f5ezbV0XO97F///lVZNxYfgizmZmZWUlG\nvmHlvjdm1k+eh83PJn7mXDt5vzXDyDesqriENT4+XnqedRiVcoDLYoPL87D5ZhqvLOexsQ0DdIAe\nryKUIYzXHUCH8crXUGy/jVcZykBGpe4b+YZVFUZl549KOcB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcaffee9f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UxaOPdqHUKBGRysHXVGCYma0JvEow2vPYaAF3Xzxikpn9EbjZ3W8qk9yaYHzJ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\nVyKvZxXmRQ0DBpnZZDObamb71rgvERERkV6j1pY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3phiLpD/L7617JH1Z0upU4pD0BUmH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\ncuPLzMzMrEH/DwswkzEh9coBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb0024c4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.plot_posteriors([\"a\", \"t\", \"v\", \"a_std\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## How well does model fit data? -> Posterior predictive plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wiecki/miniconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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20qtUqdautTP4/5sznFuZQL1t0D7YQalSeawD46k+Q7H7db5oNZaXB5MmwXHluWSRlWUn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XnGvVDKNeGeZ3zaA+PBa8BrIvI7UAYE9ZfkRih0NT4LxAHzHFdRFhpjrg5kAGYfC30FMoY6jgAu\nBJaLyFLHY/82xswOYkyuQuF9MxF4y5Gg16ELr4VyrgmF94vmmb1pnvGO5poammfqp3nGvVDONaHw\nvgEv84wuZKmUUkoppZQKGwFdyFIppZRSSimlmkIbMEoppZRSSqmwoQ0YpZRSSimlVNjQBoxSSiml\nlFIqbGgDRimllFJKKRU2tAGjlFJKKaWUChvagFFKKaWUUkqFDW3ANHMicp6IrBKRAhFZKyJH7mO7\nG0Vkh4jkisirjgWGlFJqnxx5Jd/lViEiz9SzveYZpVSDiUgPEflSRPY4csizIhK9j201z0QAbcA0\nYyJyPDAZ+LsxJhk4CljvZrsTgUnAsUB3YH/g3gCGqpQKQ8aYZGNMS2NMS6ADUAy8525bzTNKqSZ4\nHsjA5pmDgZHA1XU30jwTObQB07zdC9xrjPkFwBizwxiz3c12fwdeMcasMsbkAPcBEwIXplIqApwN\nZBhjvt/H85pnlFKN1QN41xhTZozJAGYD/dxsp3kmQmgDpplydK0eArQXkTUissXR5ZrgZvO+wG8u\n95cD6SKSFohYlVIR4e/AtHqe1zyjlGqsp4DzRKSFiHQGxgKz3GyneSZCaAOm+UoHYoGzgCOxXa6D\ngTvcbJsM5Lrcz3N8benPAJVSkUFEugNHA2/Ws5nmGaVUYy0A+mPzxhbgV2PMp2620zwTIbQB03wV\nO74+a4zJMMZkAU8A49xsWwCkuNxPdXzN92N8SqnIcRGwwBizqZ5tNM8opRpMRKKwQ8Y+BBKBtkBr\nEXnYzeaaZyKENmCaKWNMNrDVy81XYHtonAZhx7Jn+zwwpVQkupj6e19A84xSqnFaA12BKcaYcmPM\nHuAN3F+Q1TwTIbQB07y9DkwUkXaO8Z83Ap+52W4acJmI9HFsd6fjtUopVS8RORzoBLzvYVPNM0qp\nBjPG7AY2AP8UkWgRaYWdc/ebm801z0QIbcA0b/cDvwKrgZXAYuBBEenmWLOhC4AxZg7wCPANsBFY\nB9wdlIiVUuHmYuBDY0yh64OaZ5RSPnQmduL+LmANUArcqHkmcokxpv4NRF4DTgIyjTED3Dx/AXAL\nINgxhP80xiz3Q6xKqQglImOwVWSisSUuH67zfCowHTtMIAZ4zBjzRqDjVEqFN801SkUGbxowR2En\nPU3bRwNmBLDSGJPrSAz3GGOG+yVapVTEcZT0/gsYDWzD9gqeb4xZ5bLNbUBLY8y/RaStY/t0Y0xF\nMGJWSoUfzTVKRQ6PQ8iMMQuAfU5uMsYsNMY4S9L9DHTxUWxKqebhMGCtMWajMaYcmAGcVmebKmoq\nx6QAWXpCoZRqIM01SkUIX8+BuQz40sf7VEpFts7Yuv1OWx2PuZoC9BWR7diJmdcHKDalVOTQXKNU\nhPBZA0ZEjgEuBSb5ap9KqWah/nGs1hhgiTGmE7YE5nMioguPKaUaQnONUhEixhc7EZGBwMvAmH3V\n0hYRbxKHUqoJjDES7BgaYRt2wqxTV/Zeo2gC8BCAMWadiGwAegOLXDfSPKOU/4VpngEf5RrNM0r5\nn6c80+QeGBHpBnwEXGiMWeshmJC53X333UGPQeMJ75hCLZ4wtgg4UER6iEgccC4ws842m7ETbxGR\ndOwJxXp3Owv23yGU3yOhFk8oxqTx1H8Lcz7LNcH+O4TyeyTU4gnFmDSe+m/e8NgDIyLvACOBtiKy\nBVsvO9bxAZ4K3AWkAS+ICEC5MeYwr46ulGr2jDEVInItMAdb2vRVY8wqEbnK8fxU7JpFb4jIcmzJ\n9luMXW1ZKaW8orlGqcjhsQFjjDnfw/OXA5f7LCKlVLNjjJkFzKrz2FSX73cAJwY6LqVUZNFcoxqk\nrAy++abmfuvWMHRo8OJR1XwyByYcjRo1Ktgh1KLxeBZqMYVaPCr0hNp7JNTigdCLSeNR4SbU3iOh\nFg80Iab8fBgzpub+ccfBV18FLx4/CbV4vOFxIUufHUjEBOpYSjVHIoIJ38m13qyQ/S/gAsfdGKAP\n0NYYk+OyjeYZpfxI84zmmWYlKwvatq2576MGjKqfN3lGGzBKRYhwPrHwZoXsOtufDNxgjBld53HN\nM0r5keYZzTPNijZggsKbPNNsh5AppUJK9QrZACLiXCHb7YkFMB54JzChKQDWrIF582ruDx8OQ4YE\nLx6lGk7zjFIRQhswSqlQ4G6F7GHuNhSRROwk26sDEJdyWrIErrmm5v7kydqAUeFG84xSEcLjOjAi\n8pqIZIjI7/Vs84yIrBGR30RksG9DVOGgpCTYEagw15DxGKcA37uOSVdKKS9onlEqQnjTA/M68Cww\nzd2TIjIO6GmMOVBEhgEvAMN9F6IKdWvXQvKg/elQsc0+0LYtbNsW3KBUuPFmhWyn86hnWMc999xT\n/f2oUaPCsrqKUqFi/vz5zJ8/P9hh+IrmGaVCUGPyjFeT+EWkB/CZMWaAm+deBL4xxrzruP8nMNIY\nk1FnO530FqHmzoWB47rQodKlAbNrV3CDaobCfHJtDHZy7XHAduAX3EyuFZFU7KrYXYwxxW72o3nG\nX959F847r+b+5MkwaVLw4lmzBs46q+b+KafAgw8GL55mQvOM5plmRSfxB0WgJvG7G1PaBchwv7mK\nNFu2QH/N5aoJvFwhG+B0YI67kwrVzJSUwO8uI5sPPjh4saiwoHlGqcjhq0n8dVtJejrbjKxZA2P0\nL66ayNMK2Y77bwJvBjIupVTk0DyjVGTwRQOm7pjSLo7H9qJjRiPTmjUggjZbAyzCxqYrpZRSSnnF\nFw2YmcC1wAwRGQ7k1J3/4uTagFGR46+/4BzzLt9zZLBDaVbqXgS49957gxeMUkoppVSAeFNG+R3g\nR6C3iGwRkUtF5CqXMaNfAutFZC0wFa2Z3qyUltr5+j+YI8ghNdjhqDAlImNE5E9HOXa3M8NFZJSI\nLBWRP0RkfoBDVEqFOc0zqqEydDZ3yPLYA2OMOd+Lba71TTgq3JSfdxFX7urNA9zBTwxnDHOCHZIK\nMyISDUwBRmOHn/4qIjNdKwOJSCvgOeBEY8xWEWnrfm9KKbU3zTOqMebNgwuDHYRyy1eT+FUzVZqZ\nw06TTjsyWcgIbcCoxjgMWGuM2QggIjOA0wDX0qbjgQ+NMVsBjDG7Ax1ks/LAA3Zym9NLLwUvFqV8\nQ/OMarAff/SiAVNSAgMH1twfPNiWnVd+5XEImVL1KSmGtfRk/5a7eS7+/4IdjgpP7kqxd66zzYFA\naxH5RkQWichFAYuuOZo9G6ZNq7lVVAQ7IqWaSvOMapDKSli6tPZjVe6KFRljL/g4b1u2uNlI+Zo2\nYFSTFBbZBky7qN3klCdRqW8p1XDe1K+LBYYA44ATgTtF5EC/RqWUiiSaZ1SDrFtnr91soEf1Y7m5\nwYtH1aZDyFSTZOXHsZu2tJJlxMVUsaKsHwPYsdfCQErVo24p9q7Yq6OutgC7HQvLFYvId8AgYE2d\n7bRcu1I+FEHl2jXPqAZZtgx25cVzAOu4mGlM5lb27IC0YAcWgRqTZ7QBo5pkbUEHerCR5CRIqqhg\nYdkI+lV9RHSwA1PhZBFwoIj0ALYD5wJ1i4d8CkxxTMSNB4YBT7jbmZZrbx5Wr4ZewQ6iGYigcu2a\nZ1SDfPcdFJTE8DCTWMOBXMczXJ/9Env2QOvWwY4usjQmz3hTRrnesoMikioin4nIMkfZwQkNC1uF\nK2NgY0lHerKWDh0gKa6chYygsjLYkalwYoypwK4lNQdYCbxrjFlVp1z7n8BsYDnwM/CyMWZlsGJW\nwffyy8GOQIUTzTOqIYyBhQuhsDyWscziMf7FV4wm07Rl7txgR6fAQw+MN2UHgWuAP4wxpzhKDv4l\nItMdyUJFsLIy2FZlGzAjRvTk/R0V/MYgqqqCHZkKN8aYWcCsOo9NrXP/MeCxQMalQtPmzbBSTytV\nA2meUd7KzITsbKioEPqykigMF/AWn1acQsEHcN55wY5QeRpC5k3ZwSogxfF9CpCljZfmoawMttGR\nMSxh8OCeJM+qYDm9KS6PISHYwSml/G7xYiguhiOPDOxxZ8zYRzUggPffh60uUxuuvhri4wMSl1Iq\nMuzebc9xkhIqiCqyyeZapjCi8idaLYGCR58neflCu7FWaQwKTw0Yd2UHh9XZZgrwmYhsB1oC5/gu\nPBXKyspgu6MHpnVrOPQQyF63hb8qDmB4sINTYUdExgBPAdHAK8aYh+s8Pwo7Rn2946EPjTEPBDRI\nVc0YuOMOKC+3i71JgCp3lJXBW29Bzxb72OC55+Dbb2vuX3qpNmBUNc0zyht5efbiTGJsTeOkN6vp\nFbWOjUWHkj3ze5K/fyeIESpPc2C8KTs4BlhijOkEHAw8JyItmxyZCnmlpbCtqhM9WUt0NBx9NAxk\nOb9X9sV4885RysFluOoYoC9wvoj0cbPpt8aYwY6bnlQEUWYmrF1rh3OtWuV5e1/5/Xebe2JiA3dM\nFRk0zyhv5efbBkxqUu3elROj55KfD5udl/b/8Y+a9bKuuSbwgTZjnnpgvCk7OAF4CMAYs05ENgC9\nsRU/atGyg5GlpAR20ZZubAagXz/4k99ZQT8yMyE9PcgBRrgIKm8K3g1XBbRCd6hYvJjq+W4zZsB9\n9wXmuHl59uvGnFb8H4/zBDcF5sAqEmieUV7JyrLnOPHRtRswI6MW8GCJYcsuRxfwUUfB+PH2+wMO\nsD3AKiA8NWC8KTu4GTvJ/wcRScc2XtbjhpYdjCy5uRBLBbHYD/gBB9gemOfNNWzYoA0Yf4ug8qbg\n3XBVAxwuIr9hL678SysEBc/KlVDeAq7MuI8LHn6LqhkQ1SHd1h71o6IiRwXEnDSWci3XMoX92eDX\nY6qIoXlGeWXdOoiKgh7dTa13zLEls/kbM8guigtecArwMITMm7KDwP3YD/ty4CvgFmPMHn8GrUJD\nQQHEUVZ9PyHBNmBW0Jf1bpuwSu2TN4MOlwBdjTGDgGeBT/wbkqpPcVUcO3bAh2Wnsl/ZaqLWrCYQ\nH/zCQjsPZk9JC67jGf7DbYC9WqqUB5pnlFe2b7dfDznE5cH4eKqiohnP27zN+KDEpWp4XMjSU9lB\nY8wO4ETfh6ZCXX5+7QYMQA82kksqP/9c06uqlBc8Dlc1xuS7fD9LRJ4XkdZ1L5joUNXA+KH4EFq2\nhE3Z3dhKZ7qwLSDHLSiwt/aJhdxW8B8OZA238yCxWdAlIBE0LxE2VFXzjPJKRoYtTFLdgGndGrKy\nKLv/UU6863Yu4XU20Y12xZAY1EgjQ2PyjMcGjFL7UlAAcVJW65pWFIa+rOTHHw8LXmAqHHkcruoY\nopppjDEichgg7np7daiq/+WQyvelh9Jjf+iX8xWfm5P5B1M9v9AH8vLsMLIeqXm0LshmAm/wKpdx\n0brvYP582LLF4z6U9yJsqKrmGeWVrCyIiYE+dUo8JCQAlHMWHzKD8zh+FQyp++Lly2HKlJr7xxxj\nJwmrfWpMntEGjGq03FyIr9MDAzCEJWxcVUbllN+JjgYOPhhGjAh8gCpsGGMqRMQ5XDUaeNU5XNXx\n/FTgbOCfIlIBFAG6lFiQvMUF9I7fSH5ZX8bIHN43ZwWsAeOcXNuxk71QfjYfcDmv8MCmO+GYeQGJ\nQYUnzTPKWxkZEBcH3bq5f348b3Mdz9Dq1xV7N2AKC2HixJr7L7+sDRg/0AaMarTs7L17YMA2YEoL\n44meeLV94JZbtAGjPPJiuOpzgJZ4CQEfchYDY1fxS2xfjuFrrudJCkgiOQDH/usviI2F8mJbPGQY\nP5Mp6fwQdSRHVH4fgAhUONM8o7yxa5fNM9HR7p8/igVk0YYfVqRyfh6kpLjfTvmPp3VglNqnnBz3\nPTCHsohfGRqEiJRS/raLtizmEHpGb2TKFEiRAobxM99wTECO7xzaEetYB2ZNbD9o05anY28JyPGV\nUpGvogLS0vb9fBSG83mHv0q68/XXgYtL1dAGjGq0nJy9J/ED9OcP1rM/BSQFISqllM+ddx48/TQA\nn3IaJzKHAX0qOOYYiIm2xTt20DEgoeTl2fKmFY7lGRKTbGNmSfmAgBxfKRX5Kh7Y6aAAACAASURB\nVCrg0t2T7Qrd+3A+7/BnSQ+mTw9gYKqaxwaMiIwRkT9FZI2ITNrHNqNEZKmI/CEi830epQpJeXmO\nIWR1xFFOf/5gGQcHISoVjrzJM47thopIhYicGcj4mr3PP4eFCwE7fOwsPuRgx8c7Ng5SySWX1ICE\nUlBgv3bvbr+2bQOJibCzqh3bA9SIUuFJ84zyRmWlvfUsXWkXvNqHwSwlhgoWLrRzZlRg1duAEZFo\nYAowBugLnC8ifeps0wo7XvQUY0x/7AQ41Qzk57sfQgYwlF9ZxKEBjkiFI2/yjMt2DwOz0ZWyAy89\nnbv7f8ACjmYcX9LFUbM4JgZSyCOPwAwCdy5kOdQxSjWhBXToAK0TS7koZSZ/3fsOvPNOQGJR4UPz\njPJWQYFtwLSOzrUPzJoF335rv//732HZMli2jG+fWkaL1Djy8+0mKrA89cAcBqw1xmw0xpQDM4DT\n6mwzHvjQGLMVwBiz2/dhqlCUn+++BwbsPBhtwCgveZNnACYCHwC7AhmcsiqSUngx8ywOiNlESwqI\ncvz3iIkJbA9MSYk95sCB9r4AEyZAWXJrFpYfygfR59khbzqrVtWmeUZ5JT8fqqqgTXSOfaBvX+jf\n337fvj0MGgSDBjH08kG0aJ1IYSG8+aa9sKICx1MDpjPgWlR/q+MxVwcCrUXkGxFZJCIX+TJA1URZ\nWfDuuzW3X3/12a736oFJSYGZM2HmTP445O98yyifHUtFNI95RkQ6Y082XnA8pP8qAqy42FYHHRL3\ne63HBdsDE8gGDNRen+HUUyEpCUpL7Wg3pdzQPKO8kpdnGyNpzh6YfUhKgrPPtmvDrFwJGzYEKEAF\neC6j7M2HNxa7js9x2AVJF4rIT8aYNU0NTvnA+vX2aqTTFVfUjL1oosLCOj0w8fFwyikAHLAFMhaX\nkUfLAA0sUWHMmzzzFHCrY3E5QYd2BFxGfiJFRXB4x1V2dQwXqeQGZAhZZaWdXNujhz15cGrfHo46\nyo5D37DBrmXZdZ97Uc2U5hnlldxcZw9M/Q0YgAsvhDfesEWN5s2Dq/wfnnLw1IDZRu3/A12xVy1c\nbQF2G2OKgWIR+Q4YBOzVgHFdubbuqpsq/BQWQvy+hpAdCh2jMllUdSjHBjiu5mL+/PnMnz8/2GH4\ngjd55hBghj2noC0wVkTKjTEz6+5M84x/zC4eSXw8HNKv2K5h7sLZA2Pw7xlfUZE9sWjffu/nxo+H\n2bPt+PX//Q8m+DGO5kTzjOaZ5iYjA0QgLqrS47Z9+9o1KufPh5nzWmgDppEak2c8NWAWAQeKSA/s\nv6xzgfPrbPMpMMUx8S0eGAY84W5nrh94Ff5KStyXUQb7ge4es43vy45kVFWp1uv2g7r/NO+9997g\nBdM0HvOMMWZ/5/ci8jrwmbuTCtA84y+zq06kZUvYf7+9n3P2wBjj3wZMYaFtwIze9Ta8+kut50aO\nhNRUuwDde++5NGAmT7ZPuDrqKDjiCD9GGjk0z2ieaW527LCl2sWLExcRuPJKWLIE1uSnu99o2jT4\n5Re4/faa8omqlsbkmXobMMaYChG5FpgDRAOvGmNWichVjuenGmP+FJHZwHKgCnjZGLPvunMqYlRV\nQUK0+wZMUhIMar2Zb3eO5JqcubQJcGwqfHiTZ4IaoKKIFnxnjmRIX2jVau/nnZP4q6r8u7iYswFz\n4oonYMXiWs/Fx8OZZ9rlapYsgSrjiGXy5L13dP/92oBpZjTPKG9lZNgGTJSXyWzcOGjTBjata0ch\niSTVHWO7YIG9XXWVNmB8yOOfxxgzyxjT2xjT0xjzkOOxqa4fdmPMY8aYfsaYAcaYZ/wZsAoRH33E\nYXnz6Fq+71lrow/ayi8cRtWsObb04M8/BzBAFU68yTMu215ijPko8FE2XzM5lcGyjIsvdv+8s4yy\nv6vwOBswqZJnH7j5Zrj00urnzz7b1hJxlkGtCTAFJk2CESP8G6AKaZpnlDeqGzBedicnJ8PFF0Ob\nuHxm4DLnuF8/mDoVuuqMPH/QkT2qcZYto2PZJtpV7NjnJgN6l9GL1azelmS7UDduDFx8Simfmc6F\nnB41kxNPdP+8swcmEA0YY2yDCYD//AduvLH6+cGD7blCURFklrl0FbVpY3tijtUZeUqp+m3ZAtHR\ndniYty6+GHom7+Rprq+pFjFypB1f1q6dP8Js9rQBoxqthAQy2vazJTicy3K76HDJOEx8C96LvSDw\nwamw42mVbBE5TUR+E5GlIvKriOgYoADYRVu+50hOjv+KznWL6DskUEIl0ZRUxfk1loKCOg2YOqKi\n4LLLoEUL+LjyVL/GosKT5hnlybZttgHTEN27wwmH5WAQ5nKC+40OPdS2iqqqmh6k0gaMarxiWlDa\nprMdHuamizRx2ACiB/RlQeXhQYhOhRMvV8n+yhgzyBgzGLgUeCXAYTZL73EOJ/EFHVsV7XMbwfbC\n5FQm+zWWXEdV031VPwQ44wxo2RLeN3/zaywq/GieUd7IyrKL5TbUmWfCzTzKo9zs+6DUXrQB00xM\n5wLKPRada5gSEkiKr6h3m3POgb+qelFBAy9nqObG4yrZxphCl7vJ2KIhys+mcTEX8BaJifVvl0Ie\neVUt/RrLrl32AmZ9QzvS0+H442EFfdlAD7/Go8KO5hnlUUGBXZyyofpcOoJ3x77J/Ojj6NGtit33\nTLFPLFqkvS5+oA2YCLc7CxYynIuYzg8c4dMx6sW08NiAOeEESJcMfmK47w6sIpHHVbIBROR0EVkF\nfI69Oqr8aBmD2EFHTmTO3hNaV6+GOXNg927AMQ/Gzw2YrCzvxqVfeimcy7u8wuV+jUeFHc0zyqPK\nSmjbtuGvkyjh5luEVq2EXbuFV19zJCvnVZeGTKpRHnlswHgaL+qy3VARqRCRM30bomqK2bPgMf5F\nDzYwi7EUFHp+jbeKaUHLhPJ6t+nXD4bGLOVzTtYLEKo+XjWtjTGfGGP6AKcDD/g3JDWVq7iSl4h2\ndxH6tddgzJjquynk+b0Bs3u3d6VNjzoKLol6k9e41Oc9zyqsaZ5RHlVUQMeOjXvt0UdD//5QWgov\nvgh79vg2NlWj3szuMl50NHYV219FZKYxZpWb7R4GZuPfdcxUA1RWwnufxvMjI5nBefwfT3DZ9in4\n6hSjhASSE+rvgYmJgXHpi3l06/lcu30FXUpL4dZbazbYbz+47jofRaTCmDerZFczxiwQkf1FpLUx\npta/CF0h2zfySWYG5zE95WrMsBOQLo4L1QceaLtWFy2q9d/ZzoHx/xCyqCjAwwLZMTEwMO4vepas\n5XNO5nTzm/5jaqTGrJAdwjTPqHpVVdlzp+7dgVUeN99LVBTcdhuMHw+ZmfD883DHHT4PM+I0Js94\nujRVPV4UQESc40Xr/lknAh8AQxt0dOVXv/4KK7LSuYqpHMM3bKMzi9e1opeP9l9MC1q2qL8BA3D8\nfuu4ZWs7vl2UxAUVFfDUUzVPHnGENmAUeLFKtogcAKw3xhgRGQLE1T2pAF0h2xcKi+BtxpOcUEn2\nc+8gF7o8efnl9nbCCTBvXvXDqeSS4+cemOxs7xowALGxcGXJS7zAPzmh+B8k+TWyyNWYFbJDmOYZ\nVa/iYtuI2VfFRW+MHg1DhsDXX8NLL+2zzpFy0Zg846kB42686DDXDUSkM7ZRcyy2AePnlQCUV4zh\n+2eWsK2wP//kBaKp4gTmsmhXN85ftAi6dYP27Zt0iBISvGrAtG9nGMeXzPhuEFpQWbnj5SrZZwEX\ni0g5UIw9+VCefPEFvOJSSOnaa+G442y5rrqOPRYmTmT7DuFJbqRXyx2ceqp3g8EDMYk/N9f71bFF\n4Bze41YmszDnII4zOjygudM8ozzJz7cNmMYOIQObo+65B5Yts70wkyfDc8+5bDBihO3Jnj69cQcY\nO9ZezQE7WefzzxsfbBjz1IDxpjHyFHCr42qFoP8jQkJpUSXd35nMkVxFF7YBMIbZfFZ6Cgwdavs1\n//nPRu/fGNsDk9Ki/jkwYIdznMJn3LBrDNnZkNboo6pIZoyZBcyq89hUl+8fAR4JdFxhb/16+OST\nmvunnGK/uj7mlJbGtm3wadaRtCSfc04sJSXFu8MEYhL/nj2OBea83D6eMibyLM+XXUG75TDIr9Gp\ncKB5RtWnoMA2YDp0aNp+RoyAUaPg00/hww9tp/Vg55O//AIlJY3f+ZIltmUETWtphTlPDRhvxose\nAsywbRfaAmNFpNwYM7PuznTMaOAsXAj/5SIu4r9UHHwIMWv+pGfMbhbkHuWT/VdWOsooe5gD43Q8\n89hZ3obPvyjnIp9EoCJsbLoKlo8+gv/9r/oS4QsvwKcVF3IPdzH85Bs9vLhGCnlsNd38FSVge2Aa\nusDcP3iRyeZWEh6Gt/f3T1xKKf9xVk8NRBGv7Gx7vKa2C0Tg/vvhxx8hIwNuugnmfvcjMRvWwMUX\n+ybYZs5TA8bjeFFjTPW/BBF5HfjMXeMFdMxoIL35JnzLSKZxMTFLcwBY+yZkTajA0PRusspK2wOT\n7KGMslNLChjLLDKuXdjEIyunCBubroLljDOqhyMUZhXz8aw9FJiWnM4nRHf2vgGTSi4rfVYixL3i\nYoiLa9hrWpHL2fIh731+GVkXQBuwP29GRs1Gbds2vGWklGqaTZsgL6/mfq9eEB9ffbe83PZevPKK\nnZPyxhv+b8Ts3GmHgLVq1fR99e5te14efxyWLoU3/xrOZYe2aPqOFeChjLIxpgJwjhddCbzrHC/q\nHDOqQk9WFnw5WziJL0iNLqh+/MgjIYZKCn0wnbWsXKgghsR4L2bTOlzJS3xccUqTj60ik6eS7SJy\ngYj8JiLLReQHERkYjDgjWeLMGbTeuYJ7uct96eR6pJBHvp8bMJWVkJra8NfdkDiVggKY+Z3jxU88\nYceIOG+ujRkV0TTPhJB//QsGDqy5bamZcl1ZaXstbrsNtm2DH36AL7/0f0gZGbYB4+3QWU9uvNFO\nOc7Ptz0y27f7Zr/Ki3VgjDGzjDG9jTE9jTEPOR6b6jpm1GXbS4wxH/kjUOW9jz+G7JwoJvBGrcc7\nd4YkKSLbB7NQCstiSKCEmFgvNn7ySfjzT45O/4u19GQtBzT5+CqyuJRsHwP0Bc4XkT51NlsPHG2M\nGQjcD7wU2CgjWEICFW3a807UBeymHafGz7FFPmK9+YBbqeSSb1r6dLHcuioroXVrLzdu187+DO3b\n03M/Q0oKvLzmGP8Fp0Ke5pnwYAzcd5+tP9KqFSQl2Y6Z22+3w0j9yVmq3VcNmNRUeOQRSEuzvTsP\nTfbNfpUXDRgVXoyBZ56BKgOj+arWULH4eN81YIpKY2hBMTHerBHXqRP07k1yShTnMYPpXOj5Naq5\nqS7ZbowpB5wl26sZYxYaY5z/vn4GugQ4xohlzh/PeaMymBA9ndyOB7Fj8Q57KXLYMM8vdrA9MMmU\nlfknxrIyO7m2TRsvX7B2rf0ZMjJI+P1XLr4Ylpf34S+fFZJXYUjzTBiYNs0W6GrZ0s51z8y0w8ny\n8uyQMn/avt02YBISfLfPcePg1FPtfmcvTffdjps5bcBEksxMfv90PZnr8tkveTdRdYrIifiuAZNX\nEksCJUQ34B0k11zDoYfH8ozcQM6IMZ5foJoTdyXb66vEfxkQgAEFzcOXX8J339lpIGeeCf36NXwf\nqeSSTwoFBZ63bYzCQtuA8boHpo7bb4cTWnzHDTy1d3nNe++tub31VlNDVaFL80womDvXrv+2dGnt\nx++7jz2jzkSuv467c24g7a+FbF9TSFERbNhgP/+//ebf0DZvdlQ69OFcGxF46CG7OOaWojb8wOEU\nFflu/82VNmAiyb/+RaczDqOkqJKfcg9yu4mvGjD5xbYHpkGuv54TPrqaorhWXFP2ZJNjUBHF64FH\nInIMcCmw1/h11XB5eXDrrbZEcXq6/b4xbAMm2W8NmJwcewLT2OWr0tPh5iMWsplufEydNXBeesku\n3HDPPY1fm0GFA80zoWDRInj2WVi3rvbj//0vrb/9mLNzX+GtPWNJLtnNk63uoUcPOyJ0zx5YudK/\noW3fjncjSxqofXtb6HFAq62M522Wb0mrroSsGscPfyYVLHl58BJXcjqf0Ia9Fg4GfNuASaDhdczT\n02HkSJj1dWefVENTEcObku04JtS+DIwxxmS725GWa2+Yu+6yxYCSk+Hqq6FLIwfMOCfxuxYV8qXs\n7KY1YACGHlLFc19dw8VM4wTmkkyh7wKMUBFWrl3zTCgZPdquS5WZSWl8CvF3TaKUOM7gY9qTyetc\nwvtyDVVV9tzB2YCprPRf0cBdu/zTgAG7TvBlf8tj84tfcEPZI7S7DN5/37fD1cJVY/KMNmDC2ZIl\ncM011Xejfvqdp1nHfEbt8yXJUYXsoZFjMFzkl8Q2vAfG4dlnYUifGBYygsObHImKEB5LtotIN+Aj\n4EJjzNp97UjLtTfMu+/a8sRDhsDEiY3fj7MBk5Xlu9hc5eTYOX5ez4FxIyYGRvEtx/E//sVjvEjj\nF/NtLiKsXLvmmVAydChcdx3l5XDd3/OZwu2cxwxaks/rXEIMlZSU2In7IrbaeUGBrUrWzU9LThUU\n+LdBcemlEP3inSxgJAsWwA032J6Z5l7FvTF5xqshZFp2METl5cFPP1XfnudqjuEb+vDnPl+SHOWb\nHpiCksb1wIAt9T6s63b+w21UeF+FWUUwL0u23wWkAS+IyFIR+SVI4Ya3yy6zhTWATNqxe7ft1Xjy\nSWjRhCUKYqgknlK/lQnds8c2YNq1a/q+nuIGZjOGLxjX9J2psKF5xg82bIDExJqbSO37s2bV+3Jj\nbKnkH3+ECbxBKfG8xQXEYE8Oxo+H1avtmirG2GIeGzb46Wd58EFOynqT27ZfY2PfssXzaxooLg6i\nqWKmOZnOuSsoeON9HngAv1ZvjFQee2Bcyg6Oxna//ioiM40xq1w2c5YdzBWRMdiyg8P9EbByr5BE\nnuD/+IrR9W7XOqGI7GIfNGBKG98DA/DcXTsZc1l/5i9OZvSUKTVPHHss9O3b5PhU+DHGzAJm1Xls\nqsv3lwOXBzquiLRjB1UIl/Eqyclw4YVwuA+6Q1PIY8eOxKbvyI1du+zXtKanL1LJ403+zvm8wyIO\npRM7mr5TFRY0z/iYMbYL15Xr/ap9rydljC0x/OGHUJgXzw468gUnEU9NKcP4eCAaHnsMTjrJViVb\nvdoORfe58nLyaUl7k7H3z+RjaeQwmzEMKVvG3OfsxaObb/b/Qp2RxJseGC07GAYeZhLH8A39WVHv\ndm0TfbQOTGkjJvG7OKiX4QHu4I7yezATJ9qxKxMn2t4kpZTfDUlazbfxJzJwINx9t2/2mUwBmzf7\nZl91bdhgy5AmNX0dXgBG8h1X8zxn8SGlxPlmp0opr/30M0ydaoeHFpXHkrn/cI7um8WSBYUwufaC\nKX362AstYHtr/CWbNHYOHmvLHjpvjZ0Y6M6AAXafL75IV7ZyZ+JjZGfbBtrkyfW291Qd3syBcVd2\nsL7FAbTsoD8UFNhC6E4uqyyt5QCe52qWcbB9oHdvOzkO7H98F+2Si1hER6Bpq0EVljZ+CJnTeN7m\ncW7iff7GOby/9wYVFXb5Wqe4ON+dvSjVjH3EGawu6UanHnG89JIdLeEL7dnFpk09fbMzV7/9RtQP\nuaTJANJ/WWBn8frAbfyHpQzmGp7jZa6wRUUyMmDmTDjwQHvW5Coryy4J7rTffvaERKlIlJNjV5N0\n2m+/hnfVfvON3c8ZZ9iv33yDWboMAX5ZEkO22I9z+/ZCcrtE3noLDjgAWOyYiPLnn7a0+fDhjBp1\nAI88Yqf/+ks2abRPq/BdUqwrKsruOz4egL8dsYPH/7TVz554wv6KHnywTiGBZctghcvFaT/3DoUL\nb3pgtOxgKPj73+0CCM7bpk2A/eNcz9NM4mG6sM1ue9ttMGWKvT3zTK3dtE/20UKWZU3rgQGIwjCF\na7mBp8hyV1jg999r/8xNmWGsQp4Xc+0OEpGFIlIiIjcFI8ZI8C1HcxVTOaT1eqZMsdc7fKWTbGfr\nXjWdfOC55xj649McVTmfLlef5nl7L0VheIMJLGUwd+OYNLp0KZx2Gvz3v3u/YMUK+5zz9sorPotF\nBYbmmQbYssV2ezhvL77Y8H08/rh9bU6OXcTlwguRD94nm1Y8XzQBsIU5evWCTz91NF5czZ1rX79g\nAf362Qn2W7f67BpGLcZADq3olBa4BkLHjjbVdOliixW8/DJcdJH9vtp779X+O7he2G3GvOmB0bKD\nIWrTJvgfl7CZblwZ/ya0aGWfiNv3cIgOLX00hKwstsk9MABH8CPn8i7X8zTTuajJ+2tOIqm8qZdz\n7bKAicDpQQgxIvzCUP7G+7zLuWRe8jhjfLyebCe283OGb/fptJUudNn7X0+TtaSAWYzlKBawK7Yz\nj3V4lKQtf/n8OCr4NM8EUEoKnHwyfPSRnbiCPSlPBb6LOprzeZeOCbmUp8Hxx9siIrU6PXr3tjP4\nFy6snrXfvr29lrl9u7117br3YZuivNz2wKSnNv3cpiGOPtq2US66CDZuhM8/h7Fj4fXX61xgGjgQ\n+ve332/fDhHy/7+xvGnAaNnBEFRYCHdNTuRLHua5xJuRHZmQ4vl1nVoVsYfWVFU1bRXTkvIY2jSx\nB8bpQW5nEL/xDudx3lNPIR9+uFfPkdpbhJU3rZ5rByAizrl21ScWxphdwC4ROSkoEYYp52d9PiM5\nh/d4lcs4lm8w5/r+WJ1kR+0rhz60jc5+acCAHfr2Qdz5DK5cwsbdB/Alx+gaVZFJ80yg3Hwz3HGH\nrXi4YwfLlsHrd8Ewzuca8zwtOrQiN6kDk/4FV1yx12h3GDPG3iZMqG7AiMChh9pJ/xs2+L4BU1wi\nFJJEu5RS3+7YC0OH2tF6F1xgO3oXL4YTT4SHHoLzjGPNvHPPtSNswDZgOncOeJyhxGMDxhhTISLO\nsoPRwKvOsoOO56dSu+wgQLkx5jD/ha3+fbvwyZq+PMaNjO2zkZZeNF4A2icVkUMrSsugCRVTKSqP\npbMPemAAEinmA85mNF9x0O8nMPj3L+GBB3yybxU2GjrXTnmyYgUV/3czmT9v5AfO5hqe413O5Rjm\nA/6pdtM5ajvl5b5faM4Y2wMziN98t9M6+qdu46QR8PPnfbmep3niof8jeuky+3sqKrJDYHTFuXCn\necYXbrjBDgcDj/MxnAtWm9NOY1PVXczhLh6Nu4OXu05hyhR74t4Qxx9vF39cudL2XPjSrvwEUsij\nRUJwahofcAAsSBnHmqRippWczAubr+C661JIaCecEZSIQptXC1lq2cHQUkYsX8yK5tCopVxe+QqS\n7H09weREu1bD7txYmnLxoqTCOQfGN2uhDjwqlbF7NnPqis/4iWE07+sKzZJWwfexDUuy6TZ3DlO4\nn7cZzxxOZDDL/HrMbjHbqaiwQ0VaN3293GoVFf4bQuYkYodxvD9iOtOX9mUMs5kx+zza4qeVOVUw\naJ7xhWXL4NtvPW62ZAl03Q3fchbXVzzNGXzMW5zH5g5H87e5kJra8EMffLCd4P799/CPfzQi9nrs\nzG1BGtn1jcL3u9iFC+hbUMBk5nN8zBxOyp/LLdmX04cPyf0ZDi6tnv/f7Pnm7FMFTBmxXMh0BlYs\n5gPObvAwh5hoSCOb7VnxTWrAlBlnFbLkJuylhgwaxMuPHcKdLZ/kmPJveHJuKSedUOGTfauw4NVc\nO28097l2ZWXw0ksw58F4svmWKonhyPZrKTv/Fjje5Yxh//2bdqAHHrBXYsHOqr3qKrrH7qCyFLKz\nfduAKSuzDZiMbofBCy7zrvcad9JAN95YU7ExLo74eBh/ZgnnLz2J23mQg1nG61zC8Xxlt0lIsONX\npk2Dd99t2rHDRCTNtUPzjG898YRtUYAd//T449VPff45/HMqdC9/jxxa8R7nMCB6JRue/Jz+R7e2\nk2EaoU8f+zFcvtwH8dexIzuBNLJDZi2WozutZ8xgiJv7B0cVL6DPnA0UHg733APjDrZDoiJFY/KM\nNmDCRGUVlJDIKXxOYVQy86uOJprGFQxPI5utu+Kb1G9eZpwLWTayAdOuHZzvMpXqkEOIj4eH+kwj\nbXkm59x+N89krueyJsSoworHuXYu6v330pzn2v34I9z+7yo2Lc9lZ95A7uc2jkv6hbivF/h+fdjD\nXEYJO0qPpUftQkwVO37P4oAD2vnsUMUlwjY603LoQTBunM/2y3HH7bU/2yaqYjL/ZjRfcQmvcyJz\neJhJpEWXETVuHKxZYxswWVnUWvimQ4d6i6iEowiba6d5Zl8yMyEvr/Zja+tMad61yz7mHDY2eDA4\n3xt79lRvtoK+3Ln0DHZWwdXyJTebR4ihEtOmPf0nHtO4+DIyYM0aksvK6J7Yjt3rE2BLrk8nwuzI\nsT0wQbF1qy124LIQTKwp46NH1rKx6AtazruU05jDsmW2KO2I/YQvqBmiV23HDjtJ2ql7d4iNDdAP\n0XiNyTNNvHylfCo93Y5jELET38Be5RRh/SfLOZLv2Ra/H6fc1IsWTZh/kkY223c3rQ+ywjSxjHLv\n3vD22zW3CRMAiI6CfzOZJyqv57Yn2vAVx9W85vXX7e/m2WebFLsKPcaYCsA5124l8K5zrp1zvp2I\ndBCRLcCNwB0isllEfNMFGOaWL7fVNc86y64Fu3/OYpZWDeQmnmDAAOP7xss+tMjN4ADWInff5dP9\n7ixIpiX5dOnoh9qp9RjN//iD/iRSRF9W8mjh1dxxB+Q5q5i+9ZY9QXDeVq2qd38quDTP1OOOO+za\nR663M8+svc3s2fbxX36pfsgYm39efRW+4jhOYSbH8T86R2fQsydMbPkGMdjPbZM6Nm691dZa7t+f\nlzJPp3PhX1Sd6ttCcZl5CcFrwIwfb3+3RUU1j23ZQlTvA9l/3lTasZsnTpjNAQfYIbVzlqVzPU/x\nc+b+PP+8bd8BcO21tf+GW7a4PVwk0B6YEFZVBb8vh0Vcyq1M5j7u4qDLcgwYNQAAIABJREFURzPq\nttHwaOP3m0Y2O7ObNhm1jKYvZFmfq3iJXqzmQqZzLu/yILc3qdGmQp8Xc+12QpNGPkaUykq7puJz\nz9nh6Dk59mSiQ1oZ8zKOtycLXbsS3aG9/4OJjoauXTGFRXTds4XNhW05woe735TXii5spV1TO3VS\nU2tfsfViUn4qeTzD9VzBy9zCI8x9CJa0OJj3SCKZQo+vV6FF84wHbdvaz8m6dfvepk0bKlNaMXd+\nCx66y06or8g5gW4cyDU8x3ucw59jb6fXtGNJOr4HZCZWv67B2re3s9s3b65ezLs/f7CSvhSXReHL\npa13F7YgzbmeXrB06mR7w9q0geTa7ebhY9NY/BbMmAFvPZFJiz+LOaXyYwpugnvvhWOOgad2Qocg\nhR5o2oDxt/vvh79c1hR4+WVo4aj/9d57dsVnJ5f6o2XlMOpI2PHzxbRnBzd0nMHIec/bK6l1u3kb\nKI1sUlZvhQun28GUPRu+cnaTe2D2xbnEblQUxzCf5Qzk4qjptJQibk1/nfu3X6blTVWz5lwn4NVX\n7cW13FzbfkhLs8PRb51YiZyEXVTBdXiTP3XsCJs3I599RpdTt7I+vxEnKvXYnJ9GF7Y26vynlptv\ntrdGGMAffJZwDof0ymfTqk50YzPjeZtLeY3BLNW8pCLDgw/ClVfamfL7WC3yoTaP8njWJeQ/ZNsU\nLVvCCWmLeG/3sdWfg8GDgSTsuNameOQRexs0qHriSwr5pJPBX4VdGNK0vdeyuzCBnsHqgXF65516\ny6u1xJadvuTEKmK6/5sbEl7k4NSN5OXZU8otUTdzFUmMZRbt2M3OndChidMdQ5XHBoyIjAGews4X\nesUY87CbbZ4BxgJFwARjzFJfBxq25s2DBQtq7r/wQs33y5bZIQguqhC+YjQP757ET1lwbtJyphec\nBn//N9H9fPMvMo1sogty7bEnTmxUA6bM+GYhy73UmT3Xhj3M7Hcblxw8lulvjWYmvzH8iQyuGG7r\nwYfKZDvVNJpnXKxYUSsvVFXZoeebNsH3q9rwePHVZBW1oLwc9kvYzhUtPmZkz20MGgQdzHbk67ZB\nDB66soWNBfv5dJ/r99gGTGxscEc9x8TYayzrr/+ShOce4xUu5yw+JIESzuJDOo37jCOGvEePboaU\ngzohE6+1F6l++qlmJ1ddZYebqaDQXOOd6vLHwBa6soCj+JaR/I/j2LWmPSTbKV8nnwynnw5H79qD\nXBC4+AaynCX5B/qmAbNiBUybRnLBQMcQsqYsMtFAixbBpEmwfn2DXhbjOHvvULKJ9VdMYtNGWLSy\nBUXrdvAJpzORZzmQNWSOaU23dju4NW0qvbsX074dtBx7JFGnneL7nyXA6m3AeLNqrYiMA3oaYw4U\nkWHAC8BwP8bsE/Pnzw9O1ZC5c2uWm11qc2LmyL9xZ84Qlq05iTXFXehmNnGpvMF1/6hg3KZXiP6y\nynflJiZMYOm7CZTn/kB9NQD2+fvZuROWLiW6qpt/emCc/vvf6m+j09KYdhJ8mXk90XOKuHrry7w6\nHHq12MLZR+7gktP20KObqV2QqHfvpldZ8iBo76EIo3mmtoIlq0l+6CEqieIverOQEfzAEXzNsZQS\nD8mVtG4NRxwBd8b9l4Fv3wpLsTc/xNNQXdnCsjLvF3fwJqaNuWkMYhXQrWnBeRPP+vXUF010tB1a\nDlu5l3u4h3v4mWF8wuk8t/0U7tzegWP4hp6xG1m/AO7K+h/9v3ZZmPfkkxvUgNE84zuRmmua+h4p\nK4OSPEgiiumfpvD62zCy8k6WcTC/MpRKojmS7zma77iOZ8g98zKS77yR/v1d1nv60CUeqPcz5AsD\n+J0leQd6vX29v6O1a+GRR4jlJVqRQyAaMPP//NP+jlassLcmSHz2EfoAfRz3r+IlyojlZ4ZxkXzK\nmi0JnL/+Jjou3sFQfqVimmH7IXDUUTBsmB2ht2XLfMaOHdWkOALNUw+Mx1VrgVOBNwGMMT+LSCsR\nSTfGZNTdWSgJ2j+Fs8+mkER+ZwCLOJQfeJvPvz2FQnmMlJQBnHHsLl77fDBigBee9v3xhw0jpztk\n/rGmcQ2YhQvhzDNJ5Ee/zoHhwgv3euiXPdu4h19Z+dCnvN1mIlsue4Uv54zlmTkjOIIfOJrvGMqv\nDGYpifdMIuHuSW527Dt6YuEzzS7PlJbaawHbttmh5r/+ajtk162D5KzBtOZHljOQBCmle+x2esRs\n44OSv3FI1SJ+em0L/cck07Il8DDwtpsD33ijHdblZTy+1IWt7DLe9wJ5E1NmRSvHGjABaMBs2OD5\n5Ovww+1QG+xV6uE7dzIotYpLt37KX0sKyF2+mZmVp/PRR/Bp5aP05jIG8Dt9WckfE9vQ9TgYMsTO\nSe7c2U472NfCn5pnfCoic42n90hpKezebXPO2rXw55/wxx92RFZmpn2+dcndZPE0cbMqKI2DEcRx\nIdN5huvoxmZ23zSZNm06ERV1ERx7JAyqc5D+/WHyZBvPvHmMOvZY3/6Q119vu6IBFi9m4PvLebHq\nSK9f7s3naGtUN9r2bg+jfTsE1m08ubmMcvy+atnPN73XcZRzFN+z9Osctvywmb7Xj2YVfVjEobxV\nMYGff7ZzKI1x9ubMJzl5FJ062Qs0ffvCQQfZxk379jVTo5paud6XPDVgvFm11t02XYCQ/bD7Q3m5\nnZqya5cddr5mjS1I0335CVRyOKulN3/IALaYLuSYVPqykiEs4UTm8Lfjspm9P0yZAnH5UXDhGJtV\nnPNB6oqJgTFjau4PGNCgWNPTIe/3lEb/rAbII5Wy/kPsuzvA4uPhkkuAax/jrqL7WDvoTL7cMZjv\n9wzjo4qz+JPeyH1C+cO2ommLFvYD2auX/VD26WPDTk+3Y3dD6QPZTIVtnqmosBVFi4tt5crsbJsD\ndu2yH+F582we2LzZnjzk5trXlJfbr5WV9h9IdLT9WMfGQp+WOdxdegvxB3Zj83/eolev1vTq1Z+E\nnpfBNhgxAjsQuj7/+Id9wwdBV7aQ2YAGjDf2VKX5dRHLBhs6dK8lxFsAvYBeixfDoYdy/sBVLHvl\nbOIm3ULZ/xbwOwNYRR/m/9GW3ctrphc4//bOv39ysm17dutm6w388YcdUdi+va0+37o1JCXZvJaQ\noPmrgcIu11RW2uq6JSVQUGCLdWRn2wZJRoatmjtrlr0AkpFhc48zzzhzjOstKqrmPRcXZ/8HHnYY\nXLHzVcYuf5jd/36SpBuvpE2HOxHnm7R/f9o95uGCYO/edigU2IR4+OG+/UVcemnN94sXM/D98awx\n3vfAeCNTOpA7ahB43y5qvPT0mt+XH6WlQdpAgEoG8AcD+IMLr2zF6n+OZNMm+/9p0SL47jvbE7d5\ns72Q9sUXtf8/ub5vnLdWrWzDpkMHm7PS0+2p6bCmrNHRAJ4aMN6uWlt3JoJfVrv9/HNYfubdRFWW\nOw4i+/xa9/u6t2+rFlB230MYhEqiqSKKSqKpJIoqoqkkmgpiqCCaCmIpI45yYikzcZQQTynxlNCC\nQhIpIpECksl3nFWkkU0a2bRlN1nsTxe2sbn7UfQ+sicTRsBx2R/Q+46/1fxgx/ZieZlj+YA2bWw2\n+uILO9TAnaQku00jHXYYTJlzKNfzFF0P/xDDx3tt80PV9zx6/949LFUGPmYheaSQ8PKU4HSsT5pk\niw84yg32/GEa1yUl/T975xkeVbU14HenExISQihSRJoIqCBYrh27oIJdQVERvZYrFuxy7aKI3YsF\nBRXEhogICqJ+goqCgCKC9F4DSWhJSJ/1/dgzpE0yk2Rqst7nOU9yztnnnJXJzJq19l6FOwF54EF4\nYRQbozqxUrqxOrsdW/a3YtvOVmz4pRm/05TdpLCHxhQSTQLZJJJFQw4QzwHiyKUBecRQQKzJJ4YC\noikkikKiKSKSYqIoIoJiFsh8sp56+eC7xiBEGAcRlb7r7AYc/JnXIJnzZ93Lyb4s2RR+hJSe+fhj\n+HTQN0RJPkVS+r8fTQHRFBBLPjHkE0ceseQ53zW5NCCSYhqSc/B9tY+tJPIth7GXY9hNE7ObZhEZ\ntIjYxSGROzk0ZhvNIjKIihSMsV8SJjcfyIFuF3P85W4ELB234erHEEK0YQvpjia8EPmQV+Mr0zWl\nyXTcSatgVweqJpF//0Wvc5tYq5MCemEnpEbGjsARGY2jGHZLEpuLW7G9uDk7cluQfqAJu/akkrml\nCfsWJPM7SaxlJ8O/30g2CWSTQAExxHOABuQSR55TX+UTSwEx5Lu+qYh2vmujTCERCCe/dBn33BPc\n1yQECBldM3MmPHnRIhoUZ1fQMYXO/2RpXZNLHIKhAblOq8NuCeSwmw20/nMaHciip9lHI/aTYvaS\nErGHppGZNI/MoGl0Ji0i04mNKDzYrcGAzfJZxMH3acO2QHDT6LyiI2vJlMbcE/Eqzc0uj33xqtQz\n4uAf3mdlcSfu8bHfFXR69izTWwYgeuxbdPtkAt0AVxesJw4c4In4/yExINHWcREH5DjiSHM0ZZej\nKemOFDLymrDHkcRuacy+jCT2rU0imwSWk8gCGrJsYTc+/zowOURGpPLPpTHmX8ATInK+c/9hwFE6\n6c0Y8zYwR0Q+de6vBE4vv9xqjPGLsaEoSgkiEnZlDVTPKEp4EY56Bnyna1TPKIr/8aRnPK3AeNO1\ndhq2MdSnTuWw112saLgqPEVR/I7qGUVRAoFPdI3qGUUJPlU6MCJSZIxxda2NBMa5utY6z48RkRnG\nmL7GmLVADjDY71IrilJnUD2jKEogUF2jKHWHKkPIFEVRFEVRFEVRQomA1i8xxgw1xqwwxiwzxlRo\nHhUsjDH3GmMcxpiUIMvxgvP1WWKMmWKMSQqSHOcbY1YaY9YYY/xfJqNqWdoYY2YbY/5xvm/uDKY8\nLowxkcaYxcaY6SEgS7IxZrLzvbPcGfZQrwlFXaN6poIcqmc8EEp6BlTXlEf1TJVyqJ5xL0/I6Zpw\n1TMBc2CMMWdg66sfLSJHAi8G6tlVYYxpA5wDbAq2LMB3QDcR6Q6sBh4OtACmpNHX+UBXYIAxpkvV\nV/mVQuAeEemGrXn2nyDL4+IuYDl+qoRVTV4DZohIF+BoyvY0qHeEoq5RPVMW1TNeE0p6BlTXHET1\njEdUz7gnFHVNWOqZQK7A3AY8JyKFACKSHsBnV8XLwAPBFgJARL4XEVe9u9+xtecDzcFGX87/lavR\nV1AQkTQR+cv5ezb2jdwyWPIAGGNaY6sPjqViuc1Ay5IEnCoi74GN8RaRfcGUKQQIRV2jeqYsqmc8\nEEp6BlTXuEH1TBWonnFPqOmacNYzgXRgOgGnGWPmG2PmGGOODeCz3WKM6Q9sFZG/gy2LG24EZgTh\nue6aeLUKghwVcFaOOQarDIPJK8D94KHwfGBoB6QbY943xvxpjHnXGBMfbKGCTEjpGtUzblE945lQ\n0jOguqY8qme8R/WMG0JE14StnvFURrlaGGO+B1q4OTXc+azGIvIvY8xxwCSgvS+fXwOZHgbOLT08\niPI8IiLTnWOGAwUi8rG/5XFDqCwhlsEYkwBMBu5yzloES44LgV0istgY0ztYcpQiCugJ3CEiC40x\nrwIPAY8FVyz/Emq6RvVMtVE9U7UcoaZnoB7qGtUzNZZH9YwHQkHXhLue8akDIyLnVHbOGHMbMMU5\nbqEzyayJiGT6UgZvZTLGHIn19JYYY8Aub/5hjDleRHYFWp5Sct2AXc47y18yeGAb0KbUfhvsrEXQ\nMMZEA18AE0VkajBlAU4C+hlj+gJxQCNjzAQRuS5I8mzFzrotdO5Pxn7Y6zShpmtUz1Qb1TNVE2p6\nBuqhrlE9UzN5Ssl1A6pnKhBCuias9UwgQ8imAmcCGGMOB2L87bxUhYgsE5HmItJORNphX7Se/vyw\ne8IYcz52Ka+/iOQFSYyDjb6MMTHYRl/TgiQLxmrjccByEXk1WHK4EJFHRKSN8z1zNfBjMD/sIpIG\nbHF+pgDOBv4JljwhQsjoGtUzlaJ6pgpCTc84ZVJdUxbVM1WgesY9oaRrwl3P+HQFxgPvAe8ZY5YC\nBUBQXyQ3hMJS4/+AGOB75yzKPBG5PZACSCWNvgIpQzlOBq4F/jbGLHYee1hEvg2iTKUJhffNUOAj\np4JehzZeC2VdEwrvF9UzFVE94x2qa0pQPVM1qmfcE8q6JhTeN+ClntFGloqiKIqiKIqihA0BbWSp\nKIqiKIqiKIpSG9SBURRFURRFURQlbFAHRlEURVEURVGUsEEdGEVRFEVRFEVRwgZ1YBRFURRFURRF\nCRvUgVEURVEURVEUJWxQB0ZRFEVRFEVRlLBBHZh6jDGmizHmR2PMXmPMGmPMxVWMvccYs8MYs88Y\nM87ZYEhRFOUgxpg7jDGLjDF5xpj3y507yxiz0hiT49Q7h1ZxnxRjzJfGmGxjzEZjzAD/S68oSjhQ\nmZ4xxkQbYyYbYzYYYxzGmNM93Ef1TBijDkw9xRgTBXwFTAMaA/8GJhpjOrkZex7wIHAm0BZoDzwZ\nOGkVRQkTtgFPY7uUH8QYkwp8AQzH6ptFwGdV3OcNIA9oBlwDvGWM6eoPgRVFCTvc6hknP2M73afh\nubO86pkwxoh4+v8qdRFjzJHAPBFJLHVsFvC7iDxWbuzHwHoR+a9z/wzgYxE5JJAyK4oSHhhjngZa\ni8hg5/6/getE5BTnfjyQAfQQkdXlrm0I7Aa6icha57HxwHYReTiAf4aiKCFMeT1T7twW4BoR+bmS\na1XPhDm6AqOUJgI40s3xrsCSUvt/A82NMY0DIpWiKOGGKbffjVI6REQOAGtxr28OB4pcRoWTJc57\nKIqiuCivZ6qD6pkwRx2Y+ssqYJcx5n5n3Oi5wGlAAzdjE4B9pfb3O38muhmrKIpSfmm/ISV6w8V+\nrG4pT4KbsVmovlEUpSy1CSFSPRPmqANTTxGRQuBi4AJgB3APMAnY6mZ4NtCo1H6S82eWP2VUFCVs\nKT8zWl6HgNUj7nRIdcYqilJ/qc0KjOqZMEcdmHqMiCwVkd4ikioifYAOwAI3Q/8BepTa7w7sFJE9\ngZBTUZSwo/zM6D9YvQEcjD/v4DxentVAlDGmY6lj3YFlvhZSUZSwpjYrMKpnwhx1YOoxxpijjDFx\nxph4Y8x9QHPgAzdDJwBDnGWXGwOPAu+7GacoSj3GGBNpjIkDooBIY0ysMSYS+BI40hhzqfP848Bf\n5RP4AUQkB5gCPOXUTacAFwEfBu4vURQlVKlCz+D8Pc45tPTvZVA9E/6oA1O/GQRsB3YCZwDniEih\nMeZQY0yWMaY1gIjMAkYBs4GNwDqsAaIoilKaR4ED2LLr1wK5wHARyQAuA0ZgK/8cC1ztusgY84gx\nZkap+9yOzcfbBUwEbhWRFQH5CxRFCXXc6hnnuVXOc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kN/EpF+ARdQCTmKitx3yG5xUge6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\nml8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CWxvY4+ST938A432eww+veQCTlQXXX08n4DZaciLfcSQLeZOruIDJ9C/fxooVcMQR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\nihn/D5ws7Z2EWCqVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb001469e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m.plot_posterior_predictive(figsize=(14, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Defining conditions with `depends_on`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 10001 of 10000 complete in 2883.5 sec"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pymc.MCMC.MCMC at 0x7fcb00146cc0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_stim = hddm.HDDM(data, depends_on={\"v\": \"stim\"})\n",
    "m_stim.find_starting_values()\n",
    "m_stim.sample(10000, burn=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Comparing drift-rates across conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fcaffed2668>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xEAu2L2DjgY2c1++8UxtYguMqA/fJPVJYZdBkbrrppoj0+8gjj0Sk36YQ05GB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\ngZmZAX8D9mZY8Q61mlEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb0040fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_WW, v_LL, v_WL = m_stim.nodes_db.node[[\"v(WW)\", \"v(LL)\", \"v(WL)\"]]\n",
    "hddm.analyze.plot_posterior_nodes([v_WW, v_LL, v_WL])\n",
    "plt.xlabel(\"drift-rate\")\n",
    "plt.ylabel(\"Posterior probability\")\n",
    "plt.title(\"Posterior of drift-rate group means\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Hypothesis testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(v_WW > v_LL) = 18.083%\n",
      "P(v_LL > v_WL) = 0.000%\n"
     ]
    }
   ],
   "source": [
    "print(\"P(v_WW > v_LL) = {:.3f}%\".format((v_WW.trace() > v_LL.trace()).mean() * 100))\n",
    "print(\"P(v_LL > v_WL) = {:.3f}%\".format((v_LL.trace() > v_WL.trace()).mean() * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Model comparison using DIC\n",
    "\n",
    "* Deviance Information Criterion.\n",
    "* Measure trading off model fit and model complexity.\n",
    "* Not perfect but useful and easy to compute.\n",
    "* Lower is better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lumped model DIC = 10960.528554\n",
      "Stimulus model DIC = 10774.454785\n"
     ]
    }
   ],
   "source": [
    "print(\"Lumped model DIC = %f\" % m.dic)\n",
    "print(\"Stimulus model DIC = %f\" % m_stim.dic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Model comparison using Posterior Predictive checks\n",
    "\n",
    "* Generate data sets from model's posterior.\n",
    "* Compare generated data sets to original data to assess if key patterns are reproduced by the model.\n",
    "* See [http://ski.clps.brown.edu/hddm_docs/tutorial_post_pred.html](http://ski.clps.brown.edu/hddm_docs/tutorial_post_pred.html) for more detail of how to do this in HDDM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Within-subject effects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Specify a glm using R-like syntax with `patsy`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (10, 3)\n",
       "  Intercept  C(stim)[T.WL]  C(stim)[T.WW]\n",
       "          1              0              0\n",
       "          1              1              0\n",
       "          1              0              1\n",
       "          1              1              0\n",
       "          1              0              1\n",
       "          1              1              0\n",
       "          1              0              0\n",
       "          1              1              0\n",
       "          1              0              1\n",
       "          1              1              0\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'C(stim)' (columns 1:3)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from patsy import dmatrix\n",
    "\n",
    "dmatrix(\"C(stim)\", data.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pass glm-descriptor to `HDDMRegressor`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding these covariates:\n",
      "['v_Intercept', \"v_C(stim, Treatment('WL'))[T.LL]\", \"v_C(stim, Treatment('WL'))[T.WW]\"]\n"
     ]
    }
   ],
   "source": [
    "m_within_subj = hddm.HDDMRegressor(data, \"v ~ C(stim, Treatment('WL'))\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 5000 of 5000 complete in 2068.6 sec"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pymc.MCMC.MCMC at 0x7f10e05beb00>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_within_subj.sample(5000, burn=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f10e19a5ac8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ITY2BLCL8/ve/54477mDGjBls2bKFuro63nzzTS6//HLA9SLZtGkTa9a4a7h+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10e1909320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_WL, v_LL, v_WW = m_within_subj.nodes_db.ix[\n",
    "    [\n",
    "        \"v_Intercept\",\n",
    "        \"v_C(stim, Treatment('WL'))[T.LL]\",\n",
    "        \"v_C(stim, Treatment('WL'))[T.WW]\",\n",
    "    ],\n",
    "    \"node\",\n",
    "]\n",
    "hddm.analyze.plot_posterior_nodes([v_WL, v_LL, v_WW])\n",
    "plt.xlabel(\"drift-rate\")\n",
    "plt.ylabel(\"Posterior probability\")\n",
    "plt.title(\"Group mean posteriors of within-subject drift-rate effects.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Fitting regression models\n",
    "\n",
    "* Main effects of theta and dbs, as well as theta x dbs interaction.\n",
    "* Within-subject effect of conflict on threshold.\n",
    "* For more information, see http://ski.clps.brown.edu/papers/Cavanagh_DBSEEG.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding these covariates:\n",
      "['a_Intercept', 'a_C(conf)[T.LC]', 'a_theta', 'a_dbs', 'a_theta:dbs']\n"
     ]
    }
   ],
   "source": [
    "m_reg = hddm.HDDMRegressor(data, \"a ~ theta*dbs + C(conf)\", depends_on={\"v\": \"stim\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 5001 of 5000 complete in 3215.8 sec"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pymc.MCMC.MCMC at 0x7f10e065d978>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_reg.sample(5000, burn=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(a_theta < 0) =  0.0289583333333\n"
     ]
    },
    {
     "data": {
      "image/png": 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M3bp1Iy4ujjPPPBOAO+64g48/PlUt7NevH0888QQ33ngjMTEx9O3bl4MHD1K+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\nQowmdqWUCjGa2JVSKsRoYldKqRDz/3oiCVmHkFUuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10e1b3c4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "theta = m_reg.nodes_db.node[\"a_theta:C(conf, Treatment('LC'))[HC]\"]\n",
    "hddm.analyze.plot_posterior_nodes([theta], bins=20)\n",
    "plt.xlabel(\"Theta coeffecient in \")\n",
    "print(\"P(a_theta < 0) = \", (theta.trace() < 0).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Outliers are a fact of life"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "outlier_data, params = hddm.generate.gen_rand_data(\n",
    "    params={\"a\": 2, \"t\": 0.4, \"v\": 0.5}, size=200, n_fast_outliers=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 2000 of 2000 complete in 7.2 sec"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pymc.MCMC.MCMC at 0x7f10e0444748>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_no_outlier = hddm.HDDM(outlier_data)\n",
    "m_no_outlier.sample(2000, burn=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Fit is strongly affected, especially by fast outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f10e1c33048>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wiecki/miniconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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TT4bvfKfjc93VSz/sMPjd7/ycW27Jf2wiEaGJbSIiIhGlJC4iIhJRSuIiIiIRpSQuIiIS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10e1959390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_no_outlier.plot_posterior_predictive()\n",
    "plt.title(\"Posterior predictive\")\n",
    "plt.xlabel(\"RT\")\n",
    "plt.ylabel(\"Probability density\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Robustness to outliers with `p_outlier`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " [-----------------100%-----------------] 2000 of 2000 complete in 7.0 sec"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<pymc.MCMC.MCMC at 0x7f10e1dc59e8>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_outlier = hddm.HDDM(outlier_data, p_outlier=0.05)\n",
    "m_outlier.sample(2000, burn=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f10e1c78e10>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wiecki/miniconda3/lib/python3.4/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  if self._edgecolors == str('face'):\n"
     ]
    },
    {
     "data": {
      "image/png": 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T3f02IAvg7mlAszFFZMiyWUh7PomPQPHm4pZ4Mgl1daqhLmNLKUn8gJlNKTwx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\n4I8fL6Fy0UhLzsR/BWyx1v4EYIx5Ezgb2FCjzVnA/wJYa/9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f10e1b63748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m_outlier.plot_posterior_predictive()\n",
    "plt.title(\"Posterior predictive\")\n",
    "plt.xlabel(\"RT\")\n",
    "plt.ylabel(\"Probability density\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Questions?\n",
    "\n",
    "## Links\n",
    "\n",
    "* Documentation: [http://ski.clps.brown.edu/hddm_docs/](http://ski.clps.brown.edu/hddm_docs/)\n",
    "* Code: https://github.com/hddm-devs/hddm\n",
    "* More info on posterior predictive checks: [http://ski.clps.brown.edu/hddm_docs/tutorial_post_pred.html](http://ski.clps.brown.edu/hddm_docs/tutorial_post_pred.html)"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
